16 Techniques to Build Real-world RAG Systems

Improving 5 pillars of RAG.

On paper, implementing a RAG system seems simple—connect a vector database, process documents, embed the data, embed the query, query the vector database, and prompt the LLM.

But in practice, turning a prototype into a high-performance application is an entirely different challenge.

We published a two-part guide that covers 16 practical techniques to build real-world RAG systems:

• ​Read part 1 here →​

• ​Read part 2 here →​

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Why care?

Many developers build their first LLM-powered tool in a few weeks but soon realize that scaling it for real-world use is where the real work begins.

Performance bottlenecks, hallucinations, and inefficient retrieval pipelines can turn an initially promising system into an unreliable one.

This guide is for those who have moved past experimentation and are now focused on building production-ready RAG applications.

We'll go beyond the basics and explore 16 practical techniques across 5 different pillars of RAG systems.

• ​Read part 1 here →​

• ​Read part 2 here →​

Of course, background on RAG is recommended, and we have already done that in our nine-part practical crash course series:

• ​RAG fundamentals​

• ​RAG evaluation​

• ​RAG optimization​

• ​Multimodal RAG​

• ​Graph RAG​

• ​Multivector retrieval using ColBERT​

• ​RAG over complex real word docs ft. ColPali

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[Recap] 5 Agentic AI design patterns explained visually

Agentic behaviors allow LLMs to refine their output by incorporating self-evaluation, planning, and collaboration!

The following visual depicts the 5 most popular design patterns employed in building AI agents.

1) Reflection pattern

The AI reviews its work to spot mistakes and iterate until it produces the final response.

2) Tool use pattern

Tools allow LLMs to gather more information by:

• Querying a vector database

• Executing Python scripts

• Invoking APIs, etc.

This is helpful since the LLM is not solely reliant on its internal knowledge.

3) ReAct (Reason and Act) pattern

ReAct combines the above two patterns:

• The Agent can reflect on the generated outputs.

• It can interact with the world using tools.

This makes it one of the most powerful patterns used today.

4) Planning pattern

Instead of solving a request in one go, the AI creates a roadmap by:

• Subdividing tasks

• Outlining objectives

This strategic thinking can solve tasks more effectively.

5) Multi-agent pattern

In this setup:

• We have several agents.

• Each Agent is assigned a dedicated role and task.

• Each Agent can also access tools.

All agents work together to deliver the final outcome while delegating tasks to other agents if needed.

We'll soon dive deep into each of these patterns, showcasing real-world use cases and code implementations.

Thanks for reading, and we'll see you next week!

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P.S. For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1.

• So many real-world NLP systems rely on pairwise context scoring. Learn scalable approaches here: Bi-encoders and Cross-encoders for Sentence Pair Similarity Scoring – Part 1.

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware.

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.