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Advanced RAG & Knowledge Systems

A RAG system has two things to prove: that retrieval finds the passage an answer needs, and that the answer stays within what that passage says. In Advanced RAG you measure both on an example system with evaluation data before you change anything. Then you add one technique at a time, from rewritten queries and enriched text passages to knowledge graphs and agentic search, and measure again on the same data after every step.
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What this module delivers.

Diagnosis first, then technique: tecnovy's Advanced RAG is for people improving an existing RAG system. You trace each failure to document preparation, retrieval or the answer and fix it there. Your team keeps only changes that measurably help.

How the training runs

Method
Optimising an existing RAG system, each technique measured before and after
Basis
tecnovy curriculum for advanced RAG techniques and evaluation
Outcome
tecnovy certificate of participation, no examination

Dates & booking

Choose a date that fits

No public dates are scheduled yet.

New dates are published regularly. Join the waitlist or plan an in-house course for your team.

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Fit

Who this module is designed for

Typical roles

  • You run or develop a RAG system whose answers are not yet good enough.
  • As an AI engineer or data scientist, you are asked to improve the retrieval quality of a knowledge system.
  • You plan knowledge systems for large document collections where answers are spread across many sources.

Prerequisites

No prerequisites

You can start right away. What you need: hands-on experience with RAG systems such as from the tecnovy RAG training, solid Python skills.

Consider instead RAG Engineering · RAG Training: Retrieval Augmented Generation for Company Knowledge For anyone who has not built a RAG system yet: you build one from the ground up, from document preparation and vector search to answers with source citations.

Curriculum

Advanced RAG in Detail

Five parts on one continuous example system: first you find out where it fails, then you improve retrieval and document preparation and move on to knowledge graphs and agentic search. Every technique is measured before and after against the same evaluation data.

01Failure analysis

You find out where an existing RAG system fails before you change anything.

  • Typical failure sources in document preparation, retrieval and the answer
  • Evaluation as the basis of every optimisation
  • Measuring the example system's baseline with its evaluation data
02Better retrieval

This part works on the query and on the result list.

  • Rewriting queries and breaking them into sub-questions
  • Tuning hybrid search (keyword and vector search combined) and reranking (re-sorting the hits)
  • Using metadata filters deliberately
  • Measuring the effect of each technique before and after
03Better document preparation

This part deals with documents that do not split cleanly into chunks.

  • Structure-aware chunking along chapters and sections
  • Preparing tables and images for retrieval
  • Enriching chunks with context from their document
04Knowledge graphs

A knowledge graph maps entities and their relationships; this part settles when that pays off.

  • When graphs help and which alternatives exist
  • Graph-based retrieval with language models
  • Weighing the effort of building the graph against the measured benefit
05Agentic search

Finally an agent does the searching, in several steps and across several sources.

  • Agents that search in several steps and combine sources
  • Cost and run time per query
  • Traceability: which searches the agent ran

Outcome

What you will be able to do afterwards

  1. 01

    You systematically trace failures in a RAG system to document preparation, retrieval or answer generation.

  2. 02

    You measure before and after every change whether retrieval finds the passages needed and the answer stays within them.

  3. 03

    You rewrite queries and break complex questions into sub-questions that are searched one by one.

  4. 04

    You tune hybrid search, reranking and metadata filters together and back their effect with measurements.

  5. 05

    You split documents along their structure and prepare tables and images for retrieval.

  6. 06

    You enrich chunks with context from their source document before they are indexed.

  7. 07

    You decide when a knowledge graph pays off and implement graph-based retrieval with language models.

  8. 08

    You build agentic search that queries in several steps and combines sources, and assess it by cost, run time and traceability.

Offer scope

What is included

Included

  • Project repository with an example system and evaluation dataYou receive the example RAG system and its evaluation data, on which you measure every technique before and after during the training.

Why tecnovy

What you get on top with us

01

No Slideshow, Hands-On!

Promised: no PowerPoint marathon. We work in groups, tie theory to practice, and you get real project examples from our experienced trainers plus the exchange with like-minded people.

02

Certificate Showroom

Get your certificate of participation and, if you have one, add your exam certificate from E-Learning. Fully automated, beautifully designed. Just for you, only at tecnovy.

03

Flexible Date Change

If you are not able to attend the course, you can rebook your training free of charge up to one week before the start of the training.

04

Attend Twice, Pay Once

You are welcome to attend the training online again within a year as a refresher.

05

Learn from Experts

We always guarantee you the use of didactically and methodically first-class qualified trainers who draw their knowledge from training experience as well as professional practical and project experience.

FAQs

Frequently asked questions

01How does Advanced RAG measure whether a technique helps?
By two separate questions: whether retrieval found the passages an answer needs, and whether every claim in the answer is supported by those passages. The evaluation framework Ragas calls these metrics context recall and faithfulness, and context recall needs reference answers to compare against (as of November 2026). In tecnovy's Advanced RAG training you measure both before and after every technique on the example system.
02How does Advanced RAG differ from the tecnovy RAG training?
The tecnovy RAG training builds a RAG system from the ground up: preparing documents, vector and hybrid search, answers with source citations. Advanced RAG starts from an existing system, finds its weak points and adds reranking, query rewriting, structure-aware chunking, knowledge graphs and agentic search, measuring each technique.
03Do I need a graph database for Advanced RAG?
Not necessarily. In tecnovy's Advanced RAG training you work out which questions a knowledge graph helps with and which alternatives exist. A graph has its price: Microsoft notes for its GraphRAG project that indexing, building the graph from the documents, can be an expensive operation (as of November 2026).
05How much experience do I need for Advanced RAG?
You should be confident programming in Python and have worked with a RAG system before, for example in the tecnovy RAG training. Advanced RAG starts from a running system, not from building blocks such as embeddings and vector search.
06What do the Advanced RAG exercises need technically?
The exercises in tecnovy's Advanced RAG training call language models through an API and need API access with a key for that. Anthropic, for example, bills such calls by the input and output tokens processed (as of November 2026); additional model calls in a multi-step agentic search increase token use.
07Will a RAG system answer without errors after Advanced RAG?
No, even an optimised RAG system can miss passages or give wrong answers. In tecnovy's Advanced RAG training you learn to measure such failures, trace them to their cause and work on them there.
08What comes after Advanced RAG?
The next step at tecnovy is AI Evaluation & Observability: there you build evaluation datasets, combine automated and human assessment and monitor AI applications in production with tracing and monitoring. Advanced RAG measures quality during the training and does not cover ongoing operation.
09Is there an examination or a certification at the end of Advanced RAG?
No, Advanced RAG is a tecnovy training with no examination and no certification. If you attend at least 80% of it, you receive the tecnovy certificate of participation.

What does your training at tecnovy look like?

Advanced RAGAdvanced RAG & Knowledge Systems

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