skip to content

Search

Systematically Improving RAG Applications

Go from RAG prototype to reliable production system using the RAG Flywheel, a proven methodology for data-driven evaluation, targeted retrieval improvements, and continuous iteration.

2 to 4 weeks Live Virtual On-site Hybrid From $4,000 USD per team (max. 10 participants per team)
Request Information View Other Programs

Who is it for?

  • Software developers building RAG applications
  • Data scientists and ML engineers
  • Product leaders overseeing AI-powered search and retrieval
  • Teams running RAG in production who want a repeatable improvement process

What You'll Achieve

  • Set up evaluation metrics and synthetic data pipelines before writing new features
  • Run the RAG Flywheel cycle to drive measurable gains sprint over sprint
  • Fine-tune embeddings and apply reranking to boost retrieval quality
  • Design feedback mechanisms that feed directly into your improvement loop
  • Build purpose-built retrievers for different content types like documents, images, tables, and structured data
  • Wire up query routing so the right retriever handles every request

Program Content

Module 1: Evaluation-First Mindset & The RAG Flywheel

  • • Moving from one-off fixes to a repeatable improvement process
  • • The RAG Flywheel: Measure → Analyze → Improve → Iterate
  • • Key retrieval metrics: precision, recall, and MRR
  • • Distinguishing leading indicators from lagging outcomes
  • • Hands-on: setting up your first evaluation pipeline

Module 2: Bootstrapping Evaluation with Synthetic Data

  • • Creating evaluation datasets when you have little or no user data
  • • Using LLMs to generate realistic query-answer pairs at scale
  • • Defining baselines so every change can be compared objectively
  • • Benchmarking different retrieval strategies side by side

Module 3: Turning Evaluations into Retrieval Gains

  • • Reading evaluation results to pinpoint where retrieval breaks down
  • • Fine-tuning embedding models on your own domain data
  • • Choosing between re-rankers and custom embeddings for your use case
  • • Combining BM25, semantic search, and metadata filters into hybrid pipelines
  • • Running controlled experiments to validate each improvement

Module 4: User Experience & Feedback Loops

  • • Collecting actionable user feedback without adding friction
  • • Reducing perceived latency so users stay engaged
  • • Adding citations and source validation to build trust in answers
  • • Closing the loop: routing feedback back into evaluation datasets

Module 5: Understanding Users & Deciding What to Fix Next

  • • Mining query logs to find recurring failure patterns
  • • Classifying queries with few-shot classifiers and domain heuristics
  • • Prioritizing improvements by volume and business impact
  • • Identifying the small percentage of queries that cause most user dissatisfaction

Module 6: Specialized Retrieval for Different Content Types

  • • Why a single retrieval strategy falls short on heterogeneous data
  • • Handling documents, images, tables, and structured records separately
  • • Working with PDF parsers, vision models, and multimodal embeddings
  • • Integrating metadata filters and Text-to-SQL for structured queries

Module 7: Unified Architecture & Query Routing

  • • Routing incoming queries to the right specialized retriever
  • • Designing clean tool interfaces so teams can work in parallel
  • • Tracking two-level metrics: routing accuracy vs. retrieval quality
  • • Debugging end-to-end when routing and retrieval interact

Module 8: Scaling and Operating RAG in Production

  • • Keeping the improvement pace as query volume grows
  • • Reducing per-query costs without sacrificing quality
  • • Observability patterns: tracing, monitoring, and alerting
  • • Planning the next iteration of the flywheel at scale

Stop Guessing, Start Measuring

Getting a RAG demo to work is straightforward. Keeping it reliable in production is a different challenge entirely. This program gives your team a structured, metrics-driven process to identify what is failing, fix it with targeted changes, and verify the results, over and over again.

The RAG Flywheel

Everything in this training revolves around one core loop:

  1. Measure: define what good retrieval looks like and generate synthetic evaluation data to test it
  2. Analyze: dig into the results to understand exactly where and why the system falls short
  3. Improve: apply focused changes like better chunking, fine-tuned embeddings, hybrid search, and routing
  4. Iterate: fold in real user feedback, update your benchmarks, and run the cycle again

Each module walks through one piece of this loop with hands-on exercises your team can apply directly to their own system.

Grounded in Real Production Scenarios

Throughout the program we work through documented examples where teams took RAG systems from unreliable prototypes to dependable production tools. You will see how evaluation-driven decisions (not guesswork) drove each round of improvement, and apply the same patterns to your own data.

What You Get

  • Hands-on Python notebooks aligned to each module so participants practice every concept immediately
  • Live office hours for troubleshooting, architecture reviews, and Q&A
  • Supplementary lectures you can revisit anytime after the program ends
  • Industry-standard tooling: OpenAI, Anthropic, Google Gemini, Cohere, Qdrant, sentence-transformers, Instructor, Langfuse, Promptfoo, Opik, among others

Methodology

  • Project-based: participants work on a real RAG improvement challenge throughout the program
  • Evaluation-first: every proposed change is measured before and after
  • Framework-agnostic: the techniques apply regardless of your vector database or LLM provider
  • Collaborative: office hours and peer review keep the learning grounded in real problems

Modalities

  • Standard (4 weeks): one class day per week + next-day office hours and support
  • Intensive (2 weeks): morning sessions + afternoon support every day

Prerequisites

  • Have built or deployed at least a basic RAG system (prototype level is fine)
  • Working knowledge of Python
  • Familiarity with LLM APIs and vector databases

Ready to Transform Your Team?

Schedule a free consultation call to design this program tailored to your organization.

Schedule Your Free Consultation View All Training Programs