🤖Prompt Engineering: Retrieval Augmented Generation [RAG] (Just the Quotes)

"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)

"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)

 "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)

"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)

"Vector databases are designed to store and index high-dimensional embeddings - dense numeric vectors that capture the semantic meaning of text, images, audio, or other content. Instead of looking for exact matches, they use approximate nearest neighbor (ANN) algorithms to return the items whose vectors lie closest to a query vector in that multidimensional space. This makes them the engine behind semantic search, recommendation systems, image-or-audio similarity matching, and retrieval augmented generation (RAG) pipelines that supply LLM prompts with relevant context in milliseconds." (Abi Aryan, "LLMOps: Managing Large Language Models in Production", 2025)

"RAG applications must be built with semantics, metadata, and governance in mind. The retrieved information must be high-quality, secure, and appropriate for the user’s role. Equally important is monitoring and management: checking whether source data has changed, ensuring vector stores remain accurate, and watching for hallucinations or data leakage. Organizations are definitely starting to experiment with RAG models today; some are putting them into production applications. Some believe that using RAG helps mitigate hallucinations because it is grounded in trusted organizational data." (Fern Halper, "Data Makes the World Go 'Round", 2026)

"RAG is a paradigm that combines the strengths of LLMs with the rich, often unstructured data stored in a lakehouse. Rather than asking an LLM to generate responses purely from its internal parameters and training data, where knowledge can be outdated or incomplete, RAG systems first retrieve relevant documents, records, or data slices from your lakehouse and then feed those pieces into the model as context for its generative step. The result is an AI that can speak confidently about the latest reports, proprietary datasets, or domain-specific knowledge you have stored without having to retrain the model each time your data changes." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

"The foundational form of RAG, often called naive RAG, follows a straightforward pattern. A pipeline retrieves supporting context from external sources such as enterprise documents, knowledge bases, or structured datasets and appends that information to the model’s prompt before inference. In the most common implementation, each document is converted into an embedding, a numerical representation of its semantic meaning, using either the same foundation model or a specialized embedding model. When a user submits a query, the system performs a vector similarity search to find documents whose embeddings most closely match the query’s vector representation, and the retrieved content is concatenated with the user query before being passed to the language model." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 184: How to Cope With AI Invisible Prompt Injection)

Prompt Engineering Series

Prompt: "write a post of 600 words on how to cope with AI invisible prompt injection"

Introduction

Invisible prompt injection is quickly becoming one of the most challenging vulnerabilities in the era of generative AI. Unlike traditional attacks that target code, networks, or infrastructure, invisible prompt injection targets the interpretive layer of AI systems - the part that decides what the model pays attention to and how it responds. Because these manipulations are hidden inside text, images, or metadata, they are difficult to detect and even harder to prevent. Coping with this emerging threat requires a combination of technical safeguards, architectural changes, and new habits of interaction. The goal is not to eliminate the risk entirely - no system can - but to reduce its impact and build resilience into the way we use AI.

The first strategy is input sanitization, a lesson borrowed from decades of software security. Just as developers learned to sanitize user input to prevent SQL injection, AI systems must filter and clean the text they receive before interpreting it. This includes stripping out zero‑width characters, removing hidden HTML elements, and normalizing metadata. While sanitization cannot catch every attack, it dramatically reduces the surface area for invisible instructions. It creates a buffer between raw input and the model’s reasoning process, ensuring that only legitimate content reaches the interpretive layer.

A second approach is context isolation. Many prompt injection attacks succeed because AI systems treat all input as a single, unified context. If hidden instructions are embedded anywhere - inside a document, an image caption, or a webpage - the model may treat them as part of the user’s request. Context isolation breaks this assumption. By separating user instructions from external content, the system can ensure that only the user’s explicit prompt influences the model’s behavior. This can be achieved through architectural changes, such as using separate channels for instructions and data, or through interface design that clearly distinguishes between the two.

Another essential technique is retrieval‑anchored grounding. When AI systems rely solely on internal patterns, they are more vulnerable to manipulation. Retrieval‑augmented generation (RAG) forces the model to ground its answers in external sources - documents, databases, or verified knowledge. If a hidden instruction tries to steer the model toward a false claim, the retrieval layer can counterbalance it by providing factual evidence. This does not eliminate the risk, but it reduces the model’s susceptibility to manipulation by anchoring its reasoning in something more stable than raw text.

A fourth strategy involves uncertainty modeling and self‑critique. Invisible prompt injection often works because the model does not question its own reasoning. It simply follows the most salient instructions, even if they are malicious. By incorporating mechanisms that encourage the model to evaluate its own output—such as self‑critique loops, consistency checks, or multi‑agent debate frameworks—the system becomes more resistant to manipulation. When the model detects contradictions or unusual patterns in its own reasoning, it can flag the output as uncertain or request clarification from the user.

Equally important is user awareness and workflow design. Invisible prompt injection thrives in environments where users assume that AI output is always trustworthy. Coping with the threat requires a shift in mindset. Users must treat AI output as provisional, especially when working with untrusted content. Workflows should include verification steps, source inspection, and human review for high‑stakes tasks. Organizations can also implement guardrails that prevent AI systems from acting autonomously on unverified output.

Finally, coping with invisible prompt injection requires ongoing monitoring and adaptation. Attackers evolve their techniques, and defenses must evolve with them. Logging, anomaly detection, and behavioral monitoring can help identify when a system is being manipulated. Over time, these signals can inform better defenses and more robust architectures.

Invisible prompt injection is not a passing curiosity. It is a structural vulnerability that demands structural solutions. By combining technical safeguards, architectural changes, and human‑centered practices, we can build AI systems that are resilient, trustworthy, and aligned with user intent - even in the presence of invisible manipulation.

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 174: How We Can Make AI Aware of Its Knowledge Gaps)

Prompt Engineering Series

 

Prompt: "write a post of 600 words on how we can make AI aware of its knowledge gaps"

Introduction

One of the most persistent challenges in artificial intelligence is that modern systems do not know what they do not know. They generate answers with confidence, even when those answers are incomplete, speculative, or simply wrong. This inability to recognize knowledge gaps is not a minor flaw - it is a structural limitation rooted in how AI systems are built. Yet if we want AI to become a more reliable partner in reasoning, decision‑making, and discovery, we must find ways to help it identify, signal, and manage its own uncertainty. Making AI aware of its knowledge gaps is not about giving it human‑like introspection; it is about engineering mechanisms that approximate epistemic awareness.

The first step is explicit uncertainty modeling. Current AI systems generate text based on probability distributions, but they do not expose those probabilities in a meaningful way. They treat every answer as equally deliverable, regardless of how confident the underlying model actually is. By contrast, a system designed to surface its uncertainty - through calibrated confidence scores, probability ranges, or structured 'uncertainty tokens' - would be able to distinguish between strong knowledge and weak inference. This does not give the AI self‑awareness, but it gives users a window into the model’s internal landscape. When an AI can say, 'I am 40% confident in this answer', it becomes far easier to judge when to trust it and when to verify.

A second approach involves retrieval‑anchored reasoning. One of the reasons AI hallucinates is that it relies solely on internal patterns rather than external verification. Retrieval‑augmented generation (RAG) changes this dynamic by forcing the model to ground its answers in real documents, databases, or authoritative sources. When the system cannot retrieve relevant information, it can explicitly acknowledge the gap: 'I could not find supporting evidence for this claim'. This creates a form of externally enforced epistemic humility. The model becomes less of a storyteller and more of an evidence‑seeking agent.

Another promising direction is meta‑cognitive scaffolding - structures that help the AI evaluate its own reasoning steps. Chain‑of‑thought prompting, self‑critique loops, and multi‑agent debate frameworks allow the system to inspect its own output before presenting it. These mechanisms do not give the AI genuine introspection, but they simulate a process of internal review. When one reasoning path contradicts another, the system can flag the inconsistency as a potential knowledge gap. This mirrors how humans detect uncertainty: not through perfect self‑knowledge, but through the friction between competing interpretations.

We can also make AI more aware of its limits by training it on examples of uncertainty. Most datasets reward confident answers, not cautious ones. If we expose models to high‑quality examples of expert uncertainty - scientific papers that acknowledge limitations, medical notes that describe diagnostic ambiguity, or philosophical texts that explore unknowns - the AI learns that 'I don’t know' is a legitimate and often necessary response. This shifts the model’s behavior from overconfident prediction to calibrated judgment.

Equally important is user‑driven feedback. When humans correct AI errors, challenge assumptions, or request justification, they teach the system where its blind spots lie. Reinforcement learning from human feedback (RLHF) can incorporate these corrections into the model’s behavior, gradually shaping it into a system that recognizes patterns of uncertainty. Over time, the AI becomes better at identifying situations where it historically struggled - complex reasoning tasks, ambiguous queries, or domains with sparse training data.

Finally, making AI aware of its knowledge gaps requires rethinking the interface between humans and machines. If AI systems are designed to present answers as definitive, users will treat them as such. But if the interface encourages exploration - offering alternative interpretations, highlighting missing information, or prompting users to refine their questions - then uncertainty becomes part of the interaction rather than a hidden flaw.

The path to AI that understands its own limits is not about giving machines consciousness. It is about engineering systems that can recognize ambiguity, signal uncertainty, and collaborate with humans in the shared pursuit of knowledge.

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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