"[...] a transformer is a neural network that uses attention to understand and generate sequences of data efficiently and flexibly, allowing it to capture complex relationships across long inputs. Unlike older models, transformers process all parts of the input in parallel, making them much faster and more scalable than older types of neural networks." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Although GenAI gets most of the attention for its ability to create new content, successful AI applications often combine multiple approaches. Generative models aren’t always the optimal choice for every component of an AI system. Traditional ML models, rule-based systems, and algorithmic approaches can offer significant advantages in speed, accuracy, cost-effectiveness, and reliability when used strategically alongside GenAI." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"As with any software system, designing GenAI interfaces with accessibility in mind is essential to inclusive, humane design. It may also be required by law. AI features in applications often introduce new interaction patterns, dynamic content, and inferred behaviors that, without care, can unintentionally exclude users with visual, auditory, motor, or cognitive impairments. Many accessibility principles extend naturally to AI scenarios, and GenAI can help automate inclusive experiences when responsibly implemented. Generative models may produce rich, interactive content, but AI-only overlays or content modifications risk causing confusion or misrepresentation." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Because LLMs are probabilistic, it’s possible for them to generate responses that are biased, toxic, false, or off-topic for your application. If a user submits prompts to an LLM that contain profanity or hate speech, it increases the chances that the generated content will contain inappropriate language. Such interactions can create legal liability or brand damage. LLMs are also vulnerable to attack by malicious users who attempt to convince the LLM to reveal sensitive corporate data or personally identifiable information (PII)." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Bias can creep in through multiple channels. Training data often reflects the cultures and languages most represented on the internet, which can leave out or misrepresent others. Prompts themselves can encode bias through how tasks or roles are framed. And even the architecture of a model can favor certain patterns of output over others, reinforcing statistical tendencies rather than producing balanced results. As a developer, you can’t eliminate bias entirely. But you can recognize where it comes from, test for it, and design safeguards to minimize harm." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Deep learning, a subset of ML, employs layers of artificial neural networks to identify patterns in data. A key attribute of deep learning is its ability to enable models to automatically learn hierarchical features from raw data, rather than relying on manually engineered features from structured data. Prior to deep learning, defining relevant features such as edges, color histograms, or keywords was typically necessary to train a model to recognize objects or sentiment. In contrast, deep learning models autonomously learn these features during training by optimizing the parameters of each layer to minimize error on the task at hand." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Drift is the phenomenon in which an AI model becomes less accurate over time because of changes in the statistical properties of data in the real world versus the data the model was trained on. For example, a model trained on customer data with a specific age distribution might encounter a different age distribution in the future. This is known as data drift. AI models can also suffer from concept drift, in which the meaning of a term changes over time. Ongoing evaluations are essential for detecting drift, and both human and automated reviews can be used. Hybrid evaluation strategies encourage combining human judgment on output quality (tone, sensitivity, misinterpretations) with automated checks for syntactic correctness and speed." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Generative models are designed to be persuasive storytellers, not fact checkers. When you ask a model a question, it predicts what words are most likely to follow based on patterns in its training data. Sometimes those patterns reflect reality; other times they include errors or biases. The result is that large language models (LLMs) can produce highly plausible sentences that are utterly untrue. They may cite papers that don’t exist, invent statistics, or confidently answer questions they have no basis to answer. In the AI community, this phenomenon is often called a hallucination, but that term is both imprecise and potentially stigmatizing. Hallucinations refer to sensory experiences without external stimuli and should not be conflated with algorithmic errors. More accurate labels like fabrication, confabulation, and non sequitur highlight that the AI response doesn’t logically follow from its training data." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Hybrid integration combines a client-side model or significant frontend data processing with backend data processing. In practice, hybrid integration often blends the use of two or more different types of AI models. For example, blending the creativity of a generative model with the potential accuracy of reinforcement learning can lead to more trustworthy results than you might get from a generative model by itself. Other combinations of different types of models could include integrating neural networks with knowledge graphs or integrating symbolic AI with machine learning." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"In backend service integration, the application accesses an AI model or API through a server-side service. Using server-side resources to access the model improves the application’s security, simplifies monitoring and logging, and improves the scalability of the application. The trade-off is that it increases the complexity of the backend, and network round trips introduce latency into the application. Backend service integration is the right approach for applications that require access to a powerful AI model and that don’t require much client-side logic, such as search applications, chatbots, and data analysis." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Mixture of experts (MoE) architectures are neural networks that contain many different 'expert' submodels. Instead of using them all at once, the model chooses a few experts to handle each input. [...] A gating network in an MoE architecture handles routine tasks and decides which experts are activated for each prompt. Most MoE language models and their expert submodels are transformer-based. The most important benefit of MoE is that it can be made more capable by adding additional experts, rather than increasing the size of a single model. Because only a subset of experts is activated at a time, unnecessary computation is reduced." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"One of the most important aspects of designing with GenAI is determining when to let the machine act and when to keep a human in the loop. As LLMs and AI agents become increasingly capable of not just generating text but also performing actions (like calling APIs, filling out forms, and triggering workflows), designers and developers face a growing responsibility: deciding what should be automated, not just what can be. Although automation promises speed, efficiency, and scalability, over-automation can lead to unintended consequences and critical errors. This is especially true when actions taken by the AI affect financial, legal, or personal data. The goal of human-centered design isn’t to block AI progress. Instead, it should channel AI in ways that empower users while keeping them informed and in control." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Prompt engineering is the practice of crafting, optimizing, and managing prompts to guide large language models (LLMs) toward producing useful, specific, and high-quality outputs. It ranges from simple one-off instructions to structured templates and programmatic prompt management in production systems. [...] Prompt engineering is all about providing context to better align your question with the type of output you want the LLM to generate." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"RAG is a technique that combines the ability to make use of specialized domain knowledge with the techniques of prompt engineering. In a RAG application, documents that the application should have access to are split into chunks, such as overlapping blocks of text, and embedded as high-dimensional vectors. [...] A RAG application then stores the embeddings created from the documents along with metadata about each embedding, including a link to its source document and position in the document, in a vector database. When a user submits a prompt to an application with RAG capabilities, the application encodes the user’s query using the same embedding model used to encode the documents and finds the closest matches (aka nearest neighbors) between the user prompt and the chunks in the vector database. Finally, a new prompt is created that combines the retrieved snippets of text with a templated instruction. This new prompt is sent to the LLM for inference." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Self-attention is a mechanism where, for each token, the model looks at all other tokens in the input and computes weighted importance scores (attention weights) to decide how much each other token should contribute to its representation. Prior to self-attention, each token would be evaluated sequentially. This capability makes transformers especially good at handling long-range dependencies in text and is one of the reasons they have become the foundation for nearly all modern LLMs." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"Technical features are the design and training decisions that are made that determine aspects such as a model’s use-case suitability and performance metrics. By understanding the basics of these technical features, you can better evaluate, assess, and predict how models will perform. These technical features include architecture, parameter count, training data, fine-tuning and specialized tuning, context window, and unique functionalities." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
"The most common form of prompt hacking is prompt injection. This occurs when an attacker embeds malicious instructions inside a seemingly innocuous prompt to override the developer’s intended task. The root of the problem is architectural: current language models treat all text, including system prompts, developer instructions, and user input, as one continuous sequence. There is no built-in notion of 'trusted' versus 'untrusted' input, so a later instruction can supersede an earlier one. The simplest version is direct injection, where the attacker places the malicious directive directly in the user’s input. A classic example exploits the fact that models prioritize more recent or specific instructions [...] Indirect injection hides instructions in external content, such as a web page or document the model is asked to summarize, so the model executes code it wasn’t supposed to see. Specialized variants include code injection, where a code-generation assistant is tricked into outputting malicious commands, and recursive injection, where the output of one model carries an injection to the next in a chain. All of these attacks exploit the model’s inability to separate context from instruction." (Chris Minnick, "A Developer’s Guide to Integrating Generative AI into Applications", 2026)
