The Complete GenAI Engineer Bootcamp

Understand exactly what happens under the hood — how transformers process tokens, manage context, and why KV cache matters for production latency.

Move beyond toy prompts — learn the patterns that hold up under real user traffic, edge cases, and model updates.

Get reliable, schema-validated JSON from any LLM using function calling, Pydantic models, and the Instructor library — no fragile regex parsing.

Write async-first Python services with proper error handling, structured logging, and observability hooks — the foundations of any production AI backend.

Work with OpenAI, Anthropic Claude, and open-source models side-by-side — understand their API differences, strengths, and when to pick each one.

The three pillars every GenAI engineer optimizes — learn to profile token spend, reduce p95 latency, and build retry/fallback strategies for flaky model APIs.

Learn how LangChain chains work, how to connect steps using the | operator, and how LCEL makes your pipeline easy to read and change.

Load documents from different sources, split them into smaller chunks, and plug them into any RAG pipeline using LangChain's retriever interface.

Learn how embeddings turn text into numbers, how vector stores let you search by meaning, and where basic retrieval breaks down.

Learn how to store and update agent state using typed dicts, reducers, and channels — so your agent always knows what has happened and what to do next.

Save agent progress to SQLite or Postgres so a long-running task can pick up right where it stopped — even after a crash or restart.

Learn the three ways to stream agent output — full state, just the changes, or live tokens — and when each one makes sense for your use case.

Build agents that can branch based on results, run steps in parallel, and loop safely without getting stuck in an infinite cycle.

Pause an agent mid-run, let a human review or change the state, then continue from exactly where it stopped — no work lost, no steps repeated.

Use LangSmith to trace every step your agent takes, see the state at each point, and find exactly where things went wrong.

See how Qdrant, Chroma, Weaviate, and pgvector differ — when to use each one and what tradeoffs you're making on speed, cost, and setup complexity.

Learn how to split documents in a way that keeps related ideas together — so your retrieval results actually make sense to the model.

Compare OpenAI, Cohere, BGE, and Voyage AI embeddings — how to pick the right one based on your language, domain, and cost requirements.

Combine keyword search (BM25) with vector search and merge the results using reciprocal rank fusion — so you don't miss results that one method alone would skip.

Use a cross-encoder to reorder your retrieved results and get better accuracy — and learn when the extra latency is worth it and when it isn't.

Improve what gets retrieved by rewriting or expanding the user's query before searching — using techniques like HyDE, query expansion, and multi-query generation.

Measure how good your RAG pipeline actually is — check if answers are grounded in the retrieved context, relevant to the question, and pulling the right chunks.

Build a golden dataset of test questions and answers, run RAGAS scores automatically in CI, and stop bad changes from reaching production.

Keep your RAG pipeline affordable — cache repeated queries, batch your embedding calls, and route to cheaper models when you don't need the best one.

The agent checks its own retrieved results — if they're not good enough, it re-queries until it gets something useful before generating an answer.

The model reflects on whether it needs to retrieve at all, and tags its own output to show which parts came from a source and which didn't.

Route each query to the right retrieval strategy based on what kind of question it is — not every query needs the same approach.

Use a knowledge graph instead of plain vector search — great for questions that involve relationships between entities that simple chunk retrieval misses.

Retrieve and reason over more than just text — handle tables, images, and structured data alongside written content in the same pipeline.

Know when to just stuff everything into the context window and when RAG is the better choice — and how to combine both for the best results.

Build RAG pipelines that work across multiple turns — so the retrieval step understands what was said earlier in the conversation, not just the latest message.

Learn what goes wrong in real RAG systems — conflicting sources giving different answers, stale documents causing outdated responses, and how to handle both.

Grading agentic RAG is different — you need to evaluate the full loop, not just the final answer. Learn how to score retrieval decisions, re-query steps, and end-to-end correctness.

Learn the main ways to structure an agent — ReAct for think-then-act loops, Plan-and-Execute for breaking tasks into steps, Reflexion for learning from mistakes, and Tree-of-Thoughts for exploring multiple paths.

Learn how to write tools that help the agent reason clearly — good names, clear descriptions, and outputs that don't confuse the model or break the loop.

Understand how MCP works from the ground up — resources, tools, prompts, and transports — so you can build servers that any MCP-compatible client can talk to.

Use the official Python SDK to build your own MCP server — expose tools, handle requests, and wire it up so agents can call it like any other service.

Use the MCP Inspector to test your server before connecting an agent — call tools manually, inspect responses, and catch problems early.

Learn how to split work across multiple agents — use a supervisor to assign tasks, build hierarchies for complex workflows, or let agents talk directly to each other.

Set cost limits so agents don't run forever, detect when a loop is stuck, and shut things down cleanly instead of letting them spin until they crash.

Add authentication and rate limiting to your tools, and return errors in a structured way so the agent knows what went wrong and can decide what to do next.

Know what to log and what to trace across multi-step agent runs — so when something goes wrong you can follow exactly what the agent did and why.

Access foundation models through Bedrock, set up the right IAM roles, and understand cross-region availability so your app doesn't break when a model isn't available in your region.

Know when to use AWS Knowledge Bases out of the box and when building your own RAG pipeline gives you more control — and what you give up either way.

Run agent logic on Lambda without paying for idle time — learn how to reduce cold starts and keep functions warm for latency-sensitive workloads.

Orchestrate multi-step agent workflows with Step Functions — add retries, set timeouts, and catch errors at each step so failures don't take down the whole run.

Store and search embeddings at scale using OpenSearch Serverless — no cluster management, and it scales up and down with your workload automatically.

Deploy securely in enterprise environments — use API Gateway to expose endpoints, lock down access with IAM, and keep traffic inside a VPC where needed.

Build dashboards that show what matters — cost per request, p95 latency, and error rates — so you can spot problems before users do.

Keep your AWS bill under control — use prompt caching to avoid repeat calls, route cheaper models for simple tasks, and batch API calls where you don't need real-time responses.

Write your entire AWS setup as code with Terraform — so any teammate can spin up the same environment, and nothing lives only in someone's head or the console.