AI ENGINEERING & LLM MASTERCLASS

Automate evaluation to detect prompt regressions with eval suite of test cases, using a GPT-4.0 judge to score accuracy, completeness, and format, run via GitHub Actions on each prompt change.

Explore the infrastructure behind RAG, including vector databases, embeddings, and semantic search, and learn how embedding-based search overcomes keyword limitations in production AI.

Explore similarity search for embeddings: use cosine similarity for text, note dot product for normalized vectors, avoid Euclidean distance, and apply exact vs approximate search with HNSW in vector databases.

Build a production-ready ChromaDB document store with metadata, embeddings, and unique IDs, using upsert ingestion and a persistent SQLite backend, and explore collection structure and query operators.

learn how to deploy production-ready rag architectures that run reliably and efficiently, handle failures gracefully, auto-update with document changes, and keep a sensible cost profile in production.

Explore memory systems for llm prompts by applying buffer memory, summary memory, summary buffer, and token-ware approaches to manage conversation history, token costs, and context window efficiency for production readiness.

Explore document loaders and text splitters, including PyMuPDF for PDFs, unstructured for complex layouts, and web loaders, with the Inspect Docs tool, smart splitting, and metadata propagation for reliable RAG.

Learn how Lang Chain retrievers plug into RAG chains as composable runnables, starting with the vector store retriever and evolving with multi-query, contextual compression, and self-query options.

Design safe, well-described tools for AI agents to query stock, lead times, and warehouse management system data, and enforce a three-tier safety framework with structured errors and parallel tool calls.

Leverage HuggingFace PEFT with LoRa and QLoRa to fine-tune large Llama models on affordable hardware, using 4-bit NF4 quantization, targeted adapters, and scalable cloud resources.

Master production serving with vLLM, the Berkeley open-source standard that achieves 10–20x throughput and 70–90% GPU utilization via continuous batching and efficient KV cache management, with an OpenAI compatible API.

Explore cost optimization for AI projects by implementing semantic caching, model routing, prompt compression, and batch API strategies, plus practical tests and cache hit rate insights.

Implement layered guardrails for production AI, with fast input checks for prompt injections and PII, and slow output checks for schema, length, faithfulness, and context overlap.

Test llm systems with deterministic unit tests and a golden dataset of expert-verified input-output pairs; run prompt changes against it, and use field-level evalmodel scores to gate deployment in ci-cd.

Learn practical llm ops tooling to keep ai systems healthy and cost-controlled in production. Use LangSmith with LangChain for tracing prompts, responses, and latency; consider LangFuse for data residency.