Advanced

LLM Integration — Advanced

Operate LLM-powered RAG at scale: observability and tracing, fine-tuning the generator with LoRA/PEFT, inference and serving optimization, advanced retrieval-generation techniques, and a platform-scaling capstone.

5 lessons ~275 min total
  1. 1

    LLM Observability & Tracing

    You can't operate what you can't see. Trace a RAG request across every stage — retrieval, rerank, prompt assembly, generation — capturing latency, token cost, scores, and the exact context sent to the model. Langfuse, LangSmith, and the OpenTelemetry GenAI semantic conventions; spans and traces for LLM apps; logging citations and feedback; turning traces into the dataset that drives evaluation.

    50 minObservabilityTracingOpenTelemetry GenAI
  2. 2

    Fine-Tuning the Generator: LoRA & PEFT

    When prompting and RAG leave quality on the table, fine-tune the generator. Full fine-tuning vs parameter-efficient methods (LoRA, QLoRA, adapters), what fine-tuning can and can't fix (style and format yes, fresh facts no — that's RAG's job), building a training set from your traces, evaluation, and the deployment discipline that avoids catastrophic forgetting.

    55 minFine-TuningLoRAPEFT
  3. 3

    Inference & Serving Optimization

    The economics and latency of the generation call. Model selection and the build-vs-buy decision, self-hosted serving with vLLM / TGI / Ollama, PagedAttention and continuous batching, KV-cache and prompt/semantic caching, quantization for inference, and streaming — the levers that cut cost and tail latency in production.

    55 minvLLMServingCaching
  4. 4

    Advanced RAG: Long-Context, Compression & Self-Correction

    Beyond retrieve-then-generate. When to lean on long-context models vs retrieval, context compression and reordering to fight 'lost in the middle', multimodal RAG over images and tables, and self-correcting pipelines — Self-RAG and Corrective RAG (CRAG) — where the system grades its own retrieval and retries before answering.

    55 minLong-ContextMultimodal RAGSelf-RAG
  5. 5

    Capstone: Scaling & Operating a RAG Platform

    The advanced capstone and the final lesson of the LLM Integration track — synthesize observability, fine-tuning, serving, and advanced retrieval into one platform. A worked case study walking capacity planning, cost modeling across embedding/rerank/generation, multi-tenancy, SLOs and reliability, a rollout plan, and the operating playbook for running RAG at scale.

    60 minSystem DesignCapstonePlatform Ops