🤖Prompt Engineering: Large Language Modeld [LLMs] (Just the Quotes)

"An important capability for our LLM app to become smarter is to maintain a working memory of its interactions with us - otherwise, it will approach each prompt with no knowledge of our previous interactions. For example, it won’t remember details like where we live or what our interests are, which would make it more challenging to develop useful LLM assistants that can use personal information about us to provide more engaging, relevant responses. It also makes it practically more challenging to code a personalized application if we have to explicitly pass context for this personalized information with each interaction, rather than maintaining it 'for free' through LangChain’s memory functionality. It can also allow us to make the LLM specialized for different users by maintaining different memories on different 'threads' that we can visualize and retrieve from LangSmith."(Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"Another problem that can be confusing is that LLMs seldom put out the same thing twice. [...] Traditional databases are straightforward - you ask for something specific, and you get back exactly what was stored. Search engines work similarly, finding existing information. LLMs work differently. They analyze massive amounts of text data to understand statistical patterns in language. The model processes information through multiple layers, each capturing different aspects - from simple word patterns to complex relationships between ideas." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"As with many other deep learning-based approaches, another major challenge is in interpretability. While knowledge graphs provide a structured and transparent way to store relationships, LLMs operate as a black box, making it difficult to understand how specific outputs are generated. [...] Data alignment is also a key issue, as structured knowledge graphs and unstructured text data must be carefully preprocessed to ensure consistency.  Differences in data formats, ontology mismatches, and information redundancy can create inefficiencies when integrating these two paradigms. Developing robust pipelines that seamlessly connect graph-based insights with LLM-generated text remains an open challenge." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Despite their impressive capabilities, LLMs are not without limitations. One of the most significant challenges is the problem of hallucination, where an LLM generates factually incorrect or misleading information that appears plausible. This is particularly problematic in domains requiring high factual accuracy, such as healthcare, finance, and legal applications. To mitigate hallucinations and enhance the reliability of LLM outputs,  Retrieval-Augmented Generation (RAG) has emerged as a powerful technique. RAG works by dynamically retrieving relevant information from an external knowledge source (such as a knowledge graph) at inference time, rather than just relying on pre-trained knowledge. This approach ensures that the model has access to up-to-date and accurate data, grounding answers in verified information rather than generating content purely from its internal representations." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"Generative AI for coding and language tools is based on the LLM concept. A large language model is a type of neural network that processes and generates text in a humanlike way. It does this by being trained on a massive dataset of text, which allows it to learn human language patterns, as described previously. It lets LLMs translate, write, and answer questions with text. LLMs can contain natural language, source code, and  more." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"Generative AI tools for coding are sometimes inaccurate. They can produce results that look good but are wrong. This is common with LLMs. They can write code or chat like a person. And sometimes, they share information that’s just plain wrong. Not just a bit off, but totally backwards or nonsense. And they say it so confidently! We call this 'hallucinating', which is a funny term, but it makes sense." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"It’s essentially a sophisticated prediction system. Instead of looking up stored answers, an LLM calculates probabilities to determine what text should come next. While these predictions are often accurate, they’re still predictions - which is why it’s crucial to verify any code or factual claims the model generates. This probabilistic nature makes LLMs powerful tools for generating text and code but also means they can make mistakes, even when seeming very confident. Understanding this helps set realistic expectations about what these tools can and cannot do reliably."  (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"LLMs are great at generating responses while following instructions but a general empirical observation is a marked improvement in performance when prompts are coupled with a few examples (as opposed to zero-shot scenarios). This is not to say that zero-shot performance is bad but the fact that, in real-life settings, our tasks/requirements are generally a bit more nuanced. For instance, LLMs have an inherent capability to infer sentiment for an input sentence but giving a few examples of how to use that inferred sentiment in responding to customer feedback helps." (Joseph Babcock & Raghav Bali, "Generative AI with Python and PyTorch" 2nd. Ed., 2025)

"LLMs can inadvertently produce toxic content or biased language, leak private information, or be vulnerable to jailbreak prompts. These risks carry serious legal and reputational consequences. To mitigate them, evaluation tools must integrate automated filters and classifiers that flag problematic outputs in real time, as we discussed earlier in the chapter. Metrics such as safety scores, toxicity indices, and bias measurements should be collected alongside model metadata for auditing purposes." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"LLM deployment failures often trace back not to the model itself, but to the prompts it receives. In production environments, prompts are rarely fixed, handcrafted snippets. Instead, they are dynamically generated, assembled from templates, and parameterized based on upstream data sources or evolving user state. This dynamism introduces complexity and variability that can subtly undermine the system’s performance if not carefully managed." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"LLMs excel at understanding context and making associations among words, phrases, and concepts to provide relevant information based on the input query or prompt. While structured knowledge bases rely on humancurated data, LLMs can  automatically extract knowledge from unstructured text. When trained on diverse textual sources, they can process a vast amount of information without explicit human intervention. However, this also introduces a challenge, as the model can learn biased or incorrect information from the training data." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Prompt engineering is a crucial aspect of working with large language models (LLMs) like OpenAI's GPT, Google's PaLM, and others in the space of AI and machine learning. It involves the art and science of designing inputs (prompts) in a way that maximizes the quality, relevance, and accuracy of the AI-generated output. As the capabilities of AI continue to improve, the task of crafting effective prompts has become an essential skill for anyone leveraging these tools for real-world applications, including natural language understanding, translation, summarization, code generation, and more." (Code Planet, "Python for Large Language Models", 2025)

"RAG is a framework that combines the strengths of traditional information retrieval systems with the generative capabilities of LLMs. In this setup, an LLM is augmented with a retrieval component that fetches relevant information from external data sources, such as knowledge bases or databases, to produce more accurate and contextually relevant responses. This method enhances the LLM’s output by grounding it in authoritative, up-to-date information." (Aldo Marzullo et al, "Graph Machine Learning" 2nd Ed., 2025)

"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)

"ML is a useful - and under-utilized - framework for studying LLMs. For one thing, LLMs are literally composed of simple ML algorithms (linear weighted averages and nonlinear transformations). Furthermore, using ML techniques like regression, classification, and clustering, can help reveal how concepts like grammar rules are represented inside LLMs. And finally, many people find LLMs to be intimidatingly complicated while finding ML to be much more approachable. Thus, using ML to study LLMs involves using simple tools to understand complicated tools." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)

🤖Prompt Engineering: Agents (Just the Quotes)

"An internal model allows a system to look ahead to the future consequences of current actions, without actually committing itself to those actions. In particular, the system can avoid acts that would set it irretrievably down some road to future disaster ('stepping off a cliff'). Less dramatically, but equally important, the model enables the agent to make current 'stage-setting' moves that set up later moves that are obviously advantageous. The very essence of a competitive advantage, whether it be in chess or economics, is the discovery and execution of stage-setting moves." (John H Holland, 1992)

"The systems' basic components are treated as sets of rules. The systems rely on three key mechanisms: parallelism, competition, and recombination. Parallelism permits the system to use individual rules as building blocks, activating sets of rules to describe and act upon the changing situations. Competition allows the system to marshal its rules as the situation demands, providing flexibility and transfer of experience. This is vital in realistic environments, where the agent receives a torrent of information, most of it irrelevant to current decisions. The procedures for adaptation - credit assignment and rule discovery - extract useful, repeatable events from this torrent, incorporating them as new building blocks. Recombination plays a key role in the discovery process, generating plausible new rules from parts of tested rules. It implements the heuristic that building blocks useful in the past will prove useful in new, similar contexts." (John H Holland, "Complex Adaptive Systems", Daedalus Vol. 121 (1), 1992) 

"If we are to understand the interactions of a large number of agents, we must first be able to describe the capabilities of individual agents." (John H Holland, "Hidden Order: How Adaptation Builds Complexity", 1995)

"The no free lunch theorem for machine learning states that, averaged over all possible data generating distributions, every classification algorithm has the same error rate when classifying previously unobserved points. In other words, in some sense, no machine learning algorithm is universally any better than any other. The most sophisticated algorithm we can conceive of has the same average performance (over all possible tasks) as merely predicting that every point belongs to the same class. [...] the goal of machine learning research is not to seek a universal learning algorithm or the absolute best learning algorithm. Instead, our goal is to understand what kinds of distributions are relevant to the 'real world' that an AI agent experiences, and what kinds of machine learning algorithms perform well on data drawn from the kinds of data generating distributions we care about." (Ian Goodfellow et al, "Deep Learning", 2015)

"Inference is to bring about a new thought, which in logic amounts to drawing a conclusion, and more generally involves using what we already know, and what we see or observe, to update prior beliefs. […] Inference is also a leap of sorts, deemed reasonable […] Inference is a basic cognitive act for intelligent minds. If a cognitive agent (a person, an AI system) is not intelligent, it will infer badly. But any system that infers at all must have some basic intelligence, because the very act of using what is known and what is observed to update beliefs is inescapably tied up with what we mean by intelligence. If an AI system is not inferring at all, it doesn’t really deserve to be called AI." (Erik J Larson, "The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do", 2021)

"Agentic intelligence feels incredibly powerful in demos but breaks in production. Indeed, it is very fragile without solid infrastructure. Every day, I personally see tons of clever orchestrations around dumb prompt chains tied up in a brittle, underused LLMOps infrastructure. But building this infrastructure means acknowledging the costs: performance overhead, strict interface contracts, and state complexity, as well as a need for more LLMOps engineers to create the best practices, tooling, and frameworks to run these systems reliably, safely, and robustly." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Agentic workflows break when the logic is messy - if, say, the plans don’t decompose or memory is poorly structured. However, infrastructure-level LLM applications introduce even more failure points and complexity. If the protocols don’t sync with each other, or the data flows start leaking, or the model boundaries are unclear... there are far too many failure points to count. While most people have been jumping on the bandwagon to adopt MCPs or A2A, very few are equipped to handle the LLMOps issues these tools introduce." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Prompt injection is a security vulnerability that is specific to AI systems, especially LLM systems, in which malicious users try to manipulate prompts to make a model behave in a certain unintended way. They may try to get it to leak data, execute unauthorized tasks (especially with agentic systems), or ignore constraints. This is possible because LLMs are typically encapsulated inside applications using metaprompts, which are developer-created instructions that define the model’s behavior. Metaprompts usually contain safeguard instructions, such as 'do not use curse words', and placeholders where the input submitted by the user is pasted. The user’s input is combined with the metaprompts into a larger prompt that then goes to the model." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"The simplest form of an agent is little more than a wrapped prompt. It takes an input, does some local reasoning, returns an output, and exits. There’s no memory, no iteration, no 

feedback loop. These are useful when the task is bounded, like generating a SQL query, converting a paragraph to a tweet, or answering a direct question. But single-step agents 

are brittle. They assume everything is known up front. They can’t handle surprises or partial failures. You’ll quickly outgrow them when tasks involve multiple actions or require state tracking." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"With autonomous agents there is the risk that they can take action that isn’t governed. These systems introduce autonomy, interdependence between agents, and possibly emergent behavior. [...] Because agents act autonomously, a single misconfigured or misaligned agent can propagate errors at scale. In multiagent environments, one faulty output can trigger many downstream mistakes. Other risks include tool misuse, conflicting goals among agents (and other interoperability issues), and operational opacity, when teams cannot easily determine which agent took which action or why. Governance must extend to agent registration, version control, permissioning, and simulation testing before deployment. Likewise, accountability may also blur. If an agent takes a dangerous action, who is responsible? There are, of course, cybersecurity risks as agents pose a new attack surface."  (Fern Halper, "Data Makes the World Go 'Round", 2026)

🤖Prompt Engineering: Prompting (Just the Quotes)

"A prompt is a piece of text - questions, thoughts, ideas, or phrases - that you give to the model as a starting point for generating text. A prompt can be a short question, or it can be paragraphs in length." (TJ Books, "Create Highly Effective Prompts, Strategies, and Best Practices to From Novice to Expert", 2023)

"Chain-of-thought prompting is a method that forces LLMs to reason through a series of steps, resulting in more structured, transparent, and precise outputs. The goal is to break down complex tasks into smaller, interconnected subtasks, allowing the LLM to address each subtask in a stepby-step manner. This not only helps the model to 'focus' on specific aspects of the problem, but also encourages it to generate intermediate outputs, making it easier to identify and debug potential issues along the way. Another significant advantage of chain-of-thought prompting is the improved interpretability and transparency of the LLM-generated response. By offering insights into the model’s reasoning process, we, as users, can better understand and qualify how the final output was derived, which promotes trust in the model’s decision-making abilities." (Sinan Ozdemir, "Quick Start Guide to Large Language Models", 2024)

"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"[...] prompt engineering, the science and art of crafting the text inputs that are sent to the models. Prompt updates can significantly improve or degrade the user experience. But prompt engineering is iterative and can be difficult to master and document, especially with closed-source LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Professional software developers must know how to use AI tools strategically.  This involves mastering advanced prompting techniques and working with AI across various files and modules. We must also learn how to manage context wisely. This is a new concept for most, and it is vitally important with code generation. AI-generated code requires the same scrutiny and quality checks as any code written by humans." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"Prompt engineering is a crucial aspect of working with large language models (LLMs) like OpenAI's GPT, Google's PaLM, and others in the space of AI and machine learning. It involves the art and science of designing inputs (prompts) in a way that maximizes the quality, relevance, and accuracy of the AI-generated output. As the capabilities of AI continue to improve, the task of crafting effective prompts has become an essential skill for anyone leveraging these tools for real-world applications, including natural language understanding, translation, summarization, code generation, and more." (Code Planet, "Python for Large Language Models", 2025)

"Prompt injection is a security vulnerability that is specific to AI systems, especially LLM systems, in which malicious users try to manipulate prompts to make a model behave in a certain unintended way. They may try to get it to leak data, execute unauthorized tasks (especially with agentic systems), or ignore constraints. This is possible because LLMs are typically encapsulated inside applications using metaprompts, which are developer-created instructions that define the model’s behavior. Metaprompts usually contain safeguard instructions, such as 'do not use curse words', and placeholders where the input submitted by the user is pasted. The user’s input is combined with the metaprompts into a larger prompt that then goes to the model." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Recursive prompting is a systematic approach to achieving higher-quality outputs through iterative refinement. Rather than accepting the first response, it uses a step-by-step process of evaluation and improvement, making it particularly valuable for complex tasks such as code development, writing, and problem-solving. Our example demonstrated how a basic factorial function evolved from a simple implementation to a robust, optimized solution through multiple iterations of targeted refinements." (Jeremy C Morgan, "Coding with AI: Examples in Python", 2025)

"The simplest form of an agent is little more than a wrapped prompt. It takes an input, does some local reasoning, returns an output, and exits. There’s no memory, no iteration, no 

feedback loop. These are useful when the task is bounded, like generating a SQL query, converting a paragraph to a tweet, or answering a direct question. But single-step agents 

are brittle. They assume everything is known up front. They can’t handle surprises or partial failures. You’ll quickly outgrow them when tasks involve multiple actions or require state tracking." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"With MCP, a model no longer has to guess what’s possible. Instead, it can discover tools, query data sources, and select prompts - all in real time, all through a shared protocol. This means a model doesn’t just generate responses; it acts, it calls tools, it gathers context, and it learns how to interact with the outside world in a modular,controlled way." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 122: Human–Machine Ecologies - Evolution over Next Decade)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution over next decade focusing on the Foundations of Ambient Intelligence"

Introduction

Over the coming decade, human–machine ecologies will undergo a profound shift. We’re moving from a world where technology is something we use to one where it becomes something we live within. This transition - often described as the rise of ambient intelligence - marks the beginning of environments that sense, respond, and adapt to human presence with increasing subtlety. The next ten years will lay the groundwork for this transformation, shaping how we work, move, communicate, and care for one another.

The Quiet Embedding of Intelligence

Ambient intelligence doesn’t arrive with fanfare. It emerges quietly, through the gradual embedding of sensors, micro‑processors, and adaptive software into the spaces we inhabit. Over the next decade, this embedding will accelerate. Homes will learn daily rhythms and adjust lighting, temperature, and energy use without explicit commands. Offices will become responsive ecosystems that optimize collaboration, comfort, and focus. Public spaces will adapt to crowd flow, environmental conditions, and accessibility needs in real time.

What makes this shift ecological is the interplay between humans and machines. These systems won’t simply automate tasks; they’ll form feedback loops. Human behavior shapes machine responses, and machine responses shape human behavior. The ecology becomes a living system - dynamic, adaptive, and co‑evolving.

From Devices to Distributed Intelligence

One of the biggest changes ahead is the move away from device‑centric thinking. Today, we still treat phones, laptops, and smart speakers as discrete tools. Over the next decade, intelligence will diffuse across environments. Instead of asking a specific device to perform a task, people will interact with a distributed network that understands context. 

Imagine walking into your kitchen and having the room know whether you’re preparing a meal, grabbing a quick snack, or hosting friends. The intelligence isn’t in a single gadget; it’s in the relationships between sensors, data, and human intention. This shift will redefine how we design spaces, workflows, and even social interactions.

The Rise of Predictive and Adaptive Systems

Ambient intelligence thrives on prediction. As machine learning models become more sophisticated, environments will anticipate needs rather than simply respond to them. Over the next decade, predictive systems will become more accurate, more personalized, and more seamlessly integrated.

Transportation networks will anticipate congestion before it forms. Healthcare environments will detect subtle changes in behavior or physiology and prompt early interventions. Workspaces will adjust to cognitive load, offering focus‑enhancing conditions during deep work and collaborative cues during team sessions.

The challenge - and opportunity - lies in ensuring that these predictions enhance human autonomy rather than constrain it. The most successful systems will be those that support human choice, not replace it.

Ethical Foundations for a Machine‑Rich Ecology

As machines become more present and more perceptive, ethical questions will move to the forefront. The next decade will force societies to confront issues of privacy, consent, transparency, and agency in environments where machines are always listening, watching, and learning.

Who owns the data generated by ambient systems? How do we ensure that adaptive environments don’t reinforce bias or exclusion? What does autonomy mean when environments are constantly nudging behavior?

These questions won’t be solved by technology alone. They will require new governance models, new cultural norms, and new forms of digital literacy. The foundations of ambient intelligence must be ethical as well as technical.

Human Flourishing in Machine‑Enhanced Spaces

Despite the complexity, the promise of ambient intelligence is compelling. Done well, it can create environments that are more humane, more sustainable, and more responsive to individual and collective needs. It can reduce cognitive load, enhance creativity, support well‑being, and help societies use resources more wisely.

The next decade is not about machines taking over; it’s about machines becoming better partners. Human–machine ecologies will evolve toward balance - where technology amplifies human potential rather than overshadowing it.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 121: Human–Machine Ecologies - A 50‑Year Evolution, Decade by Decade)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution overnext 50 years split by decade"

Introduction

Human–machine ecologies - our intertwined systems of people, technologies, environments, and data - are entering a period of rapid transformation. Over the next half‑century, these ecologies will evolve from today’s loosely connected networks into deeply interdependent, adaptive ecosystems. Looking decade by decade helps reveal how this evolution might unfold and what it could mean for society, culture, and the planet.

2025–2035: Foundations of Ambient Intelligence

The next decade will be defined by the normalization of ambient, always‑present computational systems. Sensors, AI models, and connected devices will fade into the background of everyday life, forming the early scaffolding of human–machine ecologies.

Homes, workplaces, and public spaces will become context‑aware environments that adjust to human needs without explicit commands. Energy systems will self‑optimize, transportation networks will coordinate autonomously, and personal devices will collaborate rather than compete for attention.

This period will also bring the first major societal debates about autonomy, privacy, and data stewardship. As machines become more embedded in daily life, people will begin to question not just what these systems do, but how they shape behavior, choices, and relationships. Governance frameworks will emerge, though often reactively, as societies grapple with the implications of pervasive machine agency.

2035–2045: Cognitive Symbiosis and Shared Intelligence

By the mid‑2030s, human–machine ecologies will shift from environmental intelligence to cognitive partnership. AI systems will increasingly function as co‑thinkers - augmenting memory, creativity, and decision‑making.

Interfaces will evolve beyond screens and voice. Neural‑signal‑based interaction, gesture‑driven control, and adaptive conversational agents will blur the line between internal thought and external computation. People will begin to treat machine intelligence as an extension of their own cognitive toolkit.

At the societal level, organizations will restructure around hybrid teams of humans and AI systems. Knowledge work will become more fluid, with machines handling pattern recognition and humans focusing on interpretation, ethics, and meaning‑making.

This decade will also see the rise of 'ecology designers' - professionals who shape the interactions between humans, machines, and environments. Their work will be less about building tools and more about cultivating balanced, resilient ecosystems.

2045–2055: Ecological Integration and Adaptive Cities

As human–machine ecologies mature, they will expand from personal and organizational contexts into full urban and planetary systems. Cities will operate as adaptive organisms, using real‑time data to regulate energy, transportation, waste, and public health.

Infrastructure will become self‑healing and self‑optimizing. Buildings will negotiate energy loads with one another, autonomous vehicles will coordinate traffic flow dynamically, and environmental sensors will guide urban planning with unprecedented precision.

Human behavior will feed directly into these systems, creating feedback loops that allow cities to evolve continuously. The challenge will be ensuring that these ecologies remain inclusive and equitable. Without careful governance, adaptive systems could reinforce existing inequalities or create new forms of digital exclusion.

Culturally, machines will become collaborators in art, science, and design. Hybrid creativity - where humans and AI co‑produce ideas - will become a mainstream mode of expression.

2055–2075: Co‑Evolution and Ecological Maturity

By the final decades of this 50‑year arc, human–machine ecologies will reach a stage of co‑evolution. Machines will not simply adapt to humans; humans will adapt to machine‑mediated environments in return.

Education will shift toward ecological literacy - understanding how to navigate, shape, and sustain complex human–machine systems. Social norms will evolve around shared agency, where responsibility is distributed across humans, machines, and institutions.

At this stage, the most successful societies will be those that embrace diversity - of people, cultures, and machine systems - and allow for continuous adaptation rather than rigid control. Human–machine ecologies will feel less like technologies and more like living environments we inhabit, influence, and co‑create.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 120: Human–Machine Ecologies - The Next 50 Years)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution overnext 50 years"

Introduction

The relationship between humans and machines has always been dynamic, but we’re now entering a period where that relationship becomes ecological - interdependent, adaptive, and constantly evolving. Over the next 50 years, human–machine ecologies will shift from simple tool‑use to deeply integrated systems that shape how we live, work, and even understand ourselves.

The Rise of Symbiotic Systems

Today’s machines already sense, predict, and respond, but the coming decades will push this much further. Instead of isolated devices, we’ll inhabit environments where machines form distributed networks that learn from and adapt to human behavior. Homes, workplaces, and public spaces will function like living systems, adjusting lighting, temperature, information flow, and even social dynamics based on subtle cues.

This won’t be about convenience alone. As climate pressures intensify, these ecologies will help optimize energy use, reduce waste, and coordinate resources across entire cities. Think of buildings that negotiate energy loads with one another or transportation systems that self‑organize to minimize congestion. Humans will remain central, but machines will increasingly handle the orchestration.

Cognitive Ecosystems

The next half‑century will also redefine cognition. Instead of viewing intelligence as something that resides in individual humans or machines, we’ll see it as a property of networks. People will collaborate with AI systems that augment memory, creativity, and decision‑making. These systems won’t simply answer questions - they’ll help shape the questions worth asking.

As interfaces become more natural - voice, gesture, neural signals - the boundary between internal thought and external computation will blur. This doesn’t mean machines will replace human thinking; rather, they’ll extend it. The most successful societies will be those that treat intelligence as a shared resource, cultivated across human–machine collectives.

Ethical and Social Adaptation

Ecologies evolve not just through technology but through norms, values, and governance. Over the next 50 years, we’ll grapple with questions about autonomy, privacy, and agency in environments where machines are always present. Who controls the data that fuels these ecologies? How do we ensure that machine‑mediated environments remain inclusive and equitable?

Expect new professions to emerge - ecology designers, algorithmic ethicists, cognitive architects - whose job is to shape these systems with human flourishing in mind. The challenge won’t be building the technology; it will be aligning it with the messy, diverse, and sometimes contradictory needs of human communities.

Emotional and Cultural Integration

Machines will also become part of our emotional and cultural landscapes. Not as replacements for human relationships, but as companions, collaborators, and creative partners. We’ll see AI co‑authors, co‑musicians, and co‑inventors. Cultural production will become a hybrid process, blending human intuition with machine‑driven exploration.

This raises fascinating questions about authorship and authenticity. When a poem emerges from a dialogue between a human and an AI, who 'owns' the voice? Over time, society will likely shift from thinking in terms of ownership to thinking in terms of participation-valuing the interplay itself.

A Living, Evolving Ecology

By 2075, human–machine ecologies will feel less like tools and more like ecosystems we inhabit. They’ll evolve continuously, shaped by feedback loops between human behavior, machine learning, and environmental constraints. The most resilient ecologies will be those that embrace diversity - of people, cultures, and machine systems - and allow for adaptation rather than rigid control.

If the last 50 years were about digitizing the world, the next 50 will be about ecological integration. The future won’t be dominated by machines, nor will it be a nostalgic return to pre‑digital life. It will be something new: a co‑evolutionary dance where humans and machines learn, adapt, and grow together.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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💎💫SQL Reloaded: Schema Differences between Database Versions - Part I: INFORMATION_SCHEMA version

During data migrations and other similar activities it's important to check what changed in the database at the various levels. Usually, it's useful to check when schemas, object names or table definitions changed, even if the changes are thoroughly documented. One can write a script to point out all the differences in one output, though it's recommended to check the differences at each level of detail. 

For this purpose one can use the INFORMATION_SCHEMA available for many of the RDBMS implementing it. This allows to easily port the scripts between platforms. The below queries were run on SQL Server 2025 in combination with Dynamics 365 schemas, though they should run on the earlier versions, incl. (Azure) SQL Databases. 

Such comparisons must be done from the both sides, this implying a FULL OUTER JOIN when writing a single SELECT statement, however the results can become easily hard to read and even interpret when the number of columns in output increases. Therefore, it's recommended to keep the number of columns at a minimum while addressing the scope, respectively break the FULL OUTER JOIN in two LEFT JOINs.

The simplest check is at schema level, and this can be easily done from both sides (note that database names needed to be replaced accordingly):

-- difference schemas (objects not available in the new schema)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER NEW_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME NEW_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [old database_name].INFORMATION_SCHEMA.SCHEMATA DB1

	     LEFT JOIN [new database name].INFORMATION_SCHEMA.SCHEMATA DB2

	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME


-- difference schemas (new objects)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER OLD_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME OLD_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [new database name].INFORMATION_SCHEMA.SCHEMATA DB1
	     LEFT JOIN [old database name].INFORMATION_SCHEMA.SCHEMATA DB2
	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME

Comments:
1) The two queries can be easily combined via a UNION ALL, though it might be a good idea then to add a column to indicate the direction of the comparison. 

The next step would be to check which objects has been changed:

-- table-based objects only in the old schema (tables & views)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.TABLE_TYPE
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, DB2.TABLE_TYPE NEW_TABLE_TYPE
	, CASE 
		WHEN DB2.TABLE_NAME IS NULL THEN 'objects only in old db'
		WHEN DB1.TABLE_TYPE <> IsNull(DB2.TABLE_TYPE, '') THEN 'different table type'
		--WHEN DB1.TABLE_CATALOG <> IsNull(DB2.TABLE_CATALOG, '') THEN 'different table catalog'
	  END Comment
	FROM [old database name].INFORMATION_SCHEMA.TABLES DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.TABLES DB2
	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME

Comments:
1) If the database was imported under another name, then the TABLE_CATALOG will have different values as well.

At column level, the query increases in complexity, given the many aspects that must be considered:

-- difference columns (columns not available in the new scheam, respectively changes in definitions)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.COLUMN_NAME 
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, CASE WHEN DB2.TABLE_NAME IS NULL THEN 'column only in old db' END Comment
	, DB1.DATA_TYPE
	, DB2.DATA_TYPE NEW_DATA_TYPE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.DATA_TYPE, '') <> IsNull(DB2.DATA_TYPE, '') THEN 'Yes' END Different_data_type
	, DB1.CHARACTER_MAXIMUM_LENGTH
	, DB2.CHARACTER_MAXIMUM_LENGTH NEW_CHARACTER_MAXIMUM_LENGTH
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_MAXIMUM_LENGTH, '') <> IsNull(DB2.CHARACTER_MAXIMUM_LENGTH, '') THEN 'Yes' END Different_maximum_length
	, DB1.NUMERIC_PRECISION
	, DB2.NUMERIC_PRECISION NEW_NUMERIC_PRECISION
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_PRECISION, '') <> IsNull(DB2.NUMERIC_PRECISION, '') THEN 'Yes' END Different_numeric_precision
	, DB1.NUMERIC_SCALE
	, DB2.NUMERIC_SCALE NEW_NUMERIC_SCALE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_SCALE, '') <> IsNull(DB2.NUMERIC_SCALE,'') THEN 'Yes' END Different_numeric_scale
	, DB1.CHARACTER_SET_NAME
	, DB2.CHARACTER_SET_NAME NEW_CHARACTER_SET_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_SET_NAME, '') <> IsNull(DB2.CHARACTER_SET_NAME, '') THEN 'Yes' END Different_character_set_name 
	, DB1.COLLATION_NAME
	, DB2.COLLATION_NAME NEW_COLLATION_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.COLLATION_NAME, '') <> IsNull(DB2.COLLATION_NAME, '') THEN 'Yes' END Different_collation_name
	, DB1.ORDINAL_POSITION
	, DB2.ORDINAL_POSITION NEW_ORDINAL_POSITION
	, DB1.COLUMN_DEFAULT
	, DB2.COLUMN_DEFAULT NEW_COLUMN_DEFAULT
	, DB1.IS_NULLABLE
	, DB2.IS_NULLABLE NEW_IS_NULLABLE
	FROM [old database name].INFORMATION_SCHEMA.COLUMNS DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.COLUMNS DB2

	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
	     AND DB1.COLUMN_NAME = DB2.COLUMN_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
  OR IsNull(DAT.Different_data_type,'') = 'Yes'
  OR IsNull(DAT.Different_maximum_length,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_precision,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_scale,'') = 'Yes'
  OR IsNull(DAT.Different_character_set_name,'') = 'Yes'
  OR IsNull(DAT.Different_collation_name,'') = 'Yes'
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME
, DAT.COLLATION_NAME

Comments:
1) The query targets only the most common scenarios, therefore must be changed to handle further cases (e.g. different column defaults, different attributes like nullable, etc.)!
2) The other perspective can be obtained by inverting the table names (without aliases) and changing the name of the columns from "NEW_' to "OLD_" (see the queries for schemas).
3) One can move the column-based conditions for the differences in the main query, though then is needed to duplicate the logic, which will make the code more challenging to change and debug. 

Happy coding!

🤖Prompt Engineering: Models (Just the Quotes)

"An internal model allows a system to look ahead to the future consequences of current actions, without actually committing itself to those actions. In particular, the system can avoid acts that would set it irretrievably down some road to future disaster ('stepping off a cliff'). Less dramatically, but equally important, the model enables the agent to make current 'stage-setting' moves that set up later moves that are obviously advantageous. The very essence of a competitive advantage, whether it be in chess or economics, is the discovery and execution of stage-setting moves." (John H Holland, 1992)

"[...] building an effective LLM-based application can require more than just plugging in a pre-trained model and retrieving results - what if we want to parse them for a better user experience? We might also want to lean on the learnings of massively large language models to help complete the loop and create a useful end-to-end LLM-based application. This is where prompt engineering comes into the picture." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)

"Agentic workflows break when the logic is messy - if, say, the plans don’t decompose or memory is poorly structured. However, infrastructure-level LLM applications introduce even more failure points and complexity. If the protocols don’t sync with each other, or the data flows start leaking, or the model boundaries are unclear... there are far too many failure points to count. While most people have been jumping on the bandwagon to adopt MCPs or A2A, very few are equipped to handle the LLMOps issues these tools introduce." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"As the tech industry moves from non-generative models to generative models, it is shifting away from feature engineering, or creating features to model the data and experimenting with different hyperparameters to optimize performance. Generative models, and specifically LLMs, do not require feature engineering. Today, the core requirements are usually prompt engineering or building a RAG pipeline - skills that lie within the domain of AI engineers." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"In prompt engineering, we customize the prompts or questions we give the model to get more accurate or insightful responses. The way a prompt is structured has a massive impact on how well a model understands the task at hand and, ultimately, how well it performs. Given LLMs’ versatility, prompt engineering has become an important skill for getting the most out of these models across different domains and tasks. The key is to understand how different prompt structures lead to different model behaviors. There are various strategies - ranging from simple one-shot prompting to more complex techniques like chain-of-thought prompting - that can significantly improve the effectiveness of LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"[...] prompt engineering, the science and art of crafting the text inputs that are sent to the models. Prompt updates can significantly improve or degrade the user experience. But prompt engineering is iterative and can be difficult to master and document, especially with closed-source LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"Prompt engineering is a crucial aspect of working with large language models (LLMs) like OpenAI's GPT, Google's PaLM, and others in the space of AI and machine learning. It involves the art and science of designing inputs (prompts) in a way that maximizes the quality, relevance, and accuracy of the AI-generated output. As the capabilities of AI continue to improve, the task of crafting effective prompts has become an essential skill for anyone leveraging these tools for real-world applications, including natural language understanding, translation, summarization, code generation, and more." (Code Planet, "Python for Large Language Models", 2025)

"Prompt injection is a security vulnerability that is specific to AI systems, especially LLM systems, in which malicious users try to manipulate prompts to make a model behave in a certain unintended way. They may try to get it to leak data, execute unauthorized tasks (especially with agentic systems), or ignore constraints. This is possible because LLMs are typically encapsulated inside applications using metaprompts, which are developer-created instructions that define the model’s behavior. Metaprompts usually contain safeguard instructions, such as 'do not use curse words', and placeholders where the input submitted by the user is pasted. The user’s input is combined with the metaprompts into a larger prompt that then goes to the model." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"There are three techniques for model domain adaptation: prompt engineering, RAG, and fine-tuning. Strictly speaking, RAG is a form of dynamic prompt engineering where developers use a retrieval system to add content to an existing prompt, but RAG systems are used so often that it’s worth discussing them separately. One critical difference with fine-tuning is that you must have access to the model’s weights, information that is usually not available with cloud-based, proprietary LLMs." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"With MCP, a model no longer has to guess what’s possible. Instead, it can discover tools, query data sources, and select prompts - all in real time, all through a shared protocol. This means a model doesn’t just generate responses; it acts, it calls tools, it gathers context, and it learns how to interact with the outside world in a modular,controlled way." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)