AI & Generative AI

Generative AI went from research demo to everyday infrastructure faster than almost any technology before it. Large language models now draft code, answer support tickets, summarize documents, and drive autonomous agents — and the gap between "interesting demo" and "reliable product" comes down to engineering: grounding models in real data, constraining their outputs, and designing around their failure modes.

This hub is the practical path through that landscape — from how LLMs work, to prompting them well, to building retrieval and agent systems on top of AI APIs.

TL;DR

• LLMs are probabilistic next-token predictors — powerful, but they hallucinate and have a knowledge cutoff.
• Prompting shapes behavior; RAG grounds answers in your data; fine-tuning adapts behavior (not knowledge).
• Build with AI APIs — tool use, structured outputs, and MCP turn models into applications.
• Reach for agents only when a task is genuinely open-ended; prefer simple workflows otherwise.

The Generative AI Stack

A useful way to see how the pieces fit, from model to application:

Featured Topics

Large Language Models

• Large Language Models — Tokens, context windows, transformers, and limits
• Reasoning Models — Extended/adaptive thinking and test-time compute
• Small Language Models — Compact, on-device, and edge-ready models
• Fine-Tuning — Adapting a model to your behavior and style

Prompt Engineering & Agents

• Prompt Engineering — Designing prompts that produce reliable output
• AI Agents — LLMs that decide and act in a loop
• AI Agent Frameworks — LangGraph, agent SDKs, and orchestration
• AI Coding Assistants — Autocomplete, chat, and agentic coding
• Computer Use & Browser Agents — Agents that drive real GUIs and browsers

RAG & Embeddings

• Retrieval-Augmented Generation (RAG) — Grounding LLMs in your own data
• Agentic RAG & Advanced Retrieval — Hybrid search, reranking, GraphRAG, agentic retrieval
• Embeddings — Vectors that capture meaning
• Vector Databases — Storing and searching embeddings at scale

AI APIs & Tools

• AI APIs & Tool Use — Calling models, tool/function calling, MCP
• Model Context Protocol (MCP) — The open standard connecting models to tools and data
• Structured Outputs — Schema-valid JSON and strict tool use
• Multimodal AI — Vision, documents, and audio alongside text
• Image Generation & Diffusion Models — Text-to-image and diffusion

Building for Production

• Context Engineering — Curating the context window, caching, and memory
• LLM Evaluation — Measuring quality and catching regressions
• LLMOps — Versioning, observability, cost, and safe rollouts
• AI Guardrails & Safety — Prompt injection defense, moderation, and safe actions
• AI Gateway — Routing, fallbacks, caching, and cost control across providers
• Voice AI — Speech-to-text, TTS, and realtime voice agents
• LLM Inference & Serving — KV cache, batching, quantization, and latency tuning

Open-Source AI

• Hugging Face & Transformers — The hub of open models and the library to run them

Choosing Your Approach

The most common architectural question — prompting, RAG, or fine-tuning? — has a clear default order:

Start at the top and move down only when the simpler option falls short.

Common Questions

Where do I start building an AI feature?

With the simplest thing that works: a single, well-crafted API call. Add retrieval (RAG) when the model needs your private or current data, and reach for tool use or an agent only when the task genuinely requires the model to act over multiple steps. Most "AI features" are a good prompt plus, at most, RAG — not an autonomous agent.

How do I stop the model from making things up?

Ground it. Retrieval-augmented generation supplies real context the model must answer from, tool/function calling fetches authoritative data, and instructing the model to cite sources and say "I don't know" reduces confident fabrication. Validate critical outputs. You can lower hallucination dramatically but not eliminate it — design the product to tolerate and catch errors.

Which model should I use?

Match the tier to the task: a frontier model (e.g. Claude Opus 4.8) for the hardest reasoning and agentic work, a balanced model (Claude Sonnet 4.6) for most production workloads, and a fast/cheap model (Claude Haiku 4.5) for high-volume simple tasks. Prototype on a capable model to confirm the task is feasible, then optimize for cost/latency. See Large Language Models.

Do I need to train or fine-tune my own model?

Almost never to start. Frontier models are highly capable out of the box and adapt well through prompting and RAG — far cheaper and faster than training. Fine-tuning helps for narrow, high-volume tasks needing specific behavior or a smaller model, and only when you have quality labeled data. See Fine-Tuning.

Related Hubs

• Data Science & ML — The broader ML foundations
• Backend Development — Serving AI in production
• Python — The dominant language for AI work