GenAI Getting Started Guide

Foundation: Understanding Large Language Models

Before you write a single line of code, build a mental model of how LLMs actually work. You do not need to understand every mathematical detail, but you must grasp the core intuitions or everything that follows will feel like magic you cannot debug.

What to Learn

• Tokenization: LLMs do not see words -- they see tokens. Understanding this explains why "strawberry" letter-counting is hard, why costs vary by language, and why there are context limits.
• The Transformer Architecture: Learn attention mechanisms at a conceptual level. Understand that the model predicts the next token based on all previous tokens, weighted by learned attention patterns.
• Pre-training vs. Fine-tuning vs. RLHF: Know the three stages that turn raw text data into a helpful assistant. Pre-training gives knowledge, fine-tuning gives format, RLHF gives alignment.
• Context Windows: Every model has a maximum context. Learn what happens when you exceed it and strategies to work within limits (chunking, summarization, retrieval).
• Temperature and Sampling: Understand how temperature, top-p, and top-k control randomness. Low temperature for factual tasks, higher for creative work.
• Model Families: GPT-4, Claude, Gemini, Llama, Mistral -- know the major families, their strengths, and when to choose each.

Key Concepts

Recommended Order

Start by reading the original "Attention Is All You Need" paper summary, then experiment with the OpenAI Playground or Claude's interface. Manually adjust temperature settings to see how outputs change. Try giving the same prompt with different models to build intuition about model differences.

Prompt Engineering: Speaking the Language of AI

Prompt engineering is not just about asking nicely -- it is a systematic discipline for reliably getting the outputs you need. This is the single highest-leverage skill in the GenAI stack because it requires zero infrastructure and immediately improves every AI interaction you have.

What to Learn

• Zero-shot vs. Few-shot Prompting: Learn when to provide examples (few-shot) versus relying on instructions alone (zero-shot). Few-shot is often more reliable for structured outputs.
• Chain-of-Thought (CoT): Adding "let's think step by step" dramatically improves reasoning tasks. Learn when CoT helps and when it adds unnecessary tokens.
• System Prompts and Role Assignment: Setting context with system messages controls tone, expertise level, and output format. Learn to write system prompts that constrain behavior effectively.
• Output Formatting: Requesting JSON, Markdown, or structured formats. Learn to use schemas and validation to ensure reliable structured output.
• Prompt Chaining: Breaking complex tasks into a sequence of simpler prompts, where each step feeds the next. This is the precursor to building agents.
• Evaluation and Iteration: Learn to build prompt test suites. A prompt is only good if it works reliably across diverse inputs, not just your test case.

Key Concepts

RAG: Giving LLMs Your Own Data

Retrieval-Augmented Generation (RAG) is the most practical architecture for enterprise AI. Instead of fine-tuning a model on your data (expensive, slow, goes stale), you retrieve relevant documents at query time and include them in the prompt. This gives the LLM access to current, domain-specific information while avoiding hallucination.

What to Learn

• Document Processing Pipeline: Ingestion, cleaning, chunking. Learn different chunking strategies (fixed-size, semantic, recursive) and why chunk size dramatically affects retrieval quality.
• Embedding Models: Convert text to vectors. Understand the difference between general-purpose embeddings (OpenAI, Cohere) and domain-specific ones. Learn about embedding dimensions and their quality-cost tradeoffs.
• Vector Databases: Pinecone, Weaviate, Chroma, pgvector -- learn what a vector database does (approximate nearest neighbor search), how indexing works (HNSW, IVF), and how to choose between hosted and self-managed options.
• Retrieval Strategies: Go beyond naive similarity search. Learn hybrid search (combining keyword and semantic), re-ranking, query transformation, and multi-query retrieval for better recall.
• Context Assembly: Retrieved chunks need to be formatted, ordered, and injected into prompts intelligently. Learn about lost-in-the-middle effects and how to structure retrieved context.
• Evaluation: Measure retrieval quality (precision, recall, MRR) separately from generation quality (faithfulness, relevance). Use frameworks like RAGAS for systematic evaluation.

Key Concepts

AI Agents: Autonomous Reasoning and Action

Agents are LLMs that can reason about tasks, use tools, and take actions in a loop. This is where GenAI becomes truly powerful -- moving from single question-answer interactions to systems that can plan and execute multi-step workflows autonomously.

What to Learn

• The ReAct Pattern: Reason-Act-Observe in a loop. The LLM decides what tool to call, observes the result, then reasons about the next step. This is the foundational agent pattern.
• Tool Use / Function Calling: Give the LLM access to APIs, databases, search engines, code interpreters. Learn to define tool schemas, handle errors gracefully, and validate tool outputs.
• Planning Architectures: Plan-and-Execute separates planning from execution. The planner creates a high-level plan, then individual steps are executed, with re-planning when things go wrong.
• Multi-Agent Systems: Multiple specialized agents collaborating -- a researcher, a coder, a reviewer. Learn orchestration patterns: hierarchical, peer-to-peer, and debate-based architectures.
• Memory for Agents: Short-term (conversation), working (current task state), and long-term (persistent knowledge). Agents without memory cannot learn or maintain context across interactions.
• Guardrails and Safety: Agents acting autonomously need constraints. Learn about human-in-the-loop patterns, action budgets, sandboxing, and output validation.

Key Concepts

Production: Shipping Reliable AI Systems

The gap between a working prototype and a production system is enormous. Production AI must handle edge cases, scale to thousands of users, stay within cost budgets, and provide observability into what the model is actually doing. This is where engineering discipline meets AI experimentation.

What to Learn

• Observability and Tracing: Use tools like LangSmith, Langfuse, or Arize to trace every LLM call, retrieval, and tool use. Without observability, debugging production failures is nearly impossible.
• Cost Management: Token usage adds up fast. Learn caching strategies (semantic caching, exact-match caching), model routing (use cheaper models for simple tasks), and prompt optimization to reduce token count.
• Latency Optimization: Users expect sub-second responses. Learn streaming, parallel tool calls, speculative execution, and how to architect for responsiveness without sacrificing quality.
• Testing and Evaluation: Build comprehensive eval suites that run on every deployment. Test for regressions, hallucination rates, edge cases, and adversarial inputs. Automate this in CI/CD.
• Security: Prompt injection, data leakage, PII handling. Learn defense patterns: input sanitization, output filtering, principle of least privilege for tool access, and audit logging.
• Deployment Patterns: Blue-green deployments for prompt changes, A/B testing for model swaps, feature flags for gradual rollouts. Treat prompts as code -- version control, review, and test them.

Key Concepts