Synthora
Sign inGet started
AdvancedSelf-paced (~25 hrs) Synthora STA certified HRDF-claimable

RAG Systems & LLMOps: Build Production AI Pipelines

Connect LLMs to your private data with RAG, then ship and monitor them in production with LLMOps.

About this course

RAG (Retrieval-Augmented Generation) is the dominant enterprise LLM architecture in 2026 — enabling organisations to query their own documents, databases and knowledge bases without expensive fine-tuning. This advanced course covers end-to-end RAG system design, embedding models, vector databases, LLM evaluation frameworks, and full LLMOps pipelines for monitoring, versioning and maintaining LLM applications in production.

What you'll achieve

  • Build production-grade RAG pipelines from scratch using LangChain and LlamaIndex
  • Select and fine-tune embedding models for domain-specific retrieval
  • Implement advanced RAG techniques: HyDE, reranking, multi-hop retrieval
  • Set up LLMOps with LangSmith, MLflow and W&B for observability
  • Evaluate RAG quality using RAGAS and DeepEval frameworks
  • Deploy LLM APIs with FastAPI, Docker and cloud platforms
  • Implement guardrails, PII redaction and hallucination detection

Curriculum

  1. Module 1

    RAG Architecture & Fundamentals

    Why RAG? · Naive vs advanced RAG · Chunking strategies · Embedding models · Similarity search

  2. Module 2

    Vector Databases Deep Dive

    Pinecone · Weaviate · Qdrant · pgvector · Indexing & retrieval tuning · Hybrid search

  3. Module 3

    Advanced RAG Techniques

    HyDE · Multi-query retrieval · Reranking (Cohere) · Multi-hop reasoning · Contextual compression

  4. Module 4

    LLM Evaluation with RAGAS & DeepEval

    Faithfulness · Answer relevancy · Context recall · Custom metrics · CI evaluation gates

  5. Module 5

    LLMOps: Observability & Monitoring

    LangSmith tracing · Prompt versioning · Cost tracking · Latency optimisation · Drift detection

  6. Module 6

    Fine-Tuning & Efficient Adaptation

    When to fine-tune vs RAG · LoRA / QLoRA · Instruction tuning · PEFT with Hugging Face

  7. Module 7

    Guardrails, Safety & Compliance

    Hallucination detection · PII redaction · NeMo Guardrails · Prompt injection defence · Audit logging

  8. Module 8

    Production Deployment Patterns

    FastAPI serving · Docker & Kubernetes · AWS SageMaker / Azure ML · Caching strategies · A/B testing

  9. Module 9

    Capstone: Enterprise RAG Application

    Problem definition · Pipeline build · Evaluation · Deployment & monitoring

Who this is for

  • ML engineers & data scientists building AI products
  • Backend engineers integrating LLMs into existing systems
  • DevOps/MLOps engineers managing AI infrastructure
  • Enterprise architects designing AI knowledge management systems

Tools & technologies

LangChain LlamaIndex Pinecone Weaviate LangSmith RAGAS FastAPI Docker MLflow HuggingFace

Prerequisites

  • Python proficiency (pandas, APIs, async)
  • Basic LLM/API experience
  • Docker fundamentals
  • Cloud platform basics (AWS or Azure)