Training course · RAG & LLM Applications · Go

RAG & LLM Applications for Go Developers

Develop RAG and LLM applications: embeddings, vector databases, chunking, retrieval, evaluation, cost control and data protection.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“RAG & LLM Applications for Go Developers” is a hands-on course for roles such as Go developers in back-end and platform teams, Developers of cloud-native tools, operators and CLIs and Site reliability engineers maintaining their own Go code. Rather than learning RAG & LLM Applications in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Go.

Retrieval-augmented generation connects language models to your own knowledge: manuals, contracts, tickets, wikis or product data are made searchable and handed to the model as context at runtime. The result is assistants that give grounded answers with citations instead of hallucinating. In this two-day course you build a complete RAG pipeline, from document preparation through embeddings and a vector database to answer generation, and you learn which levers actually determine quality.

Day two takes you from prototype to robust application. You compare chunking strategies, combine semantic and lexical search, apply reranking, metadata filters and query rewriting, and measure the effect with systematic evaluations. You also cover prompt caching, cost control, latency optimisation, document-level access control and privacy-compliant operation with an eye on GDPR and the EU AI Act. By the end you know when classic RAG is enough, when a large context window or agentic retrieval is the better option, and how to justify your architecture choices.

Go is the language behind many cloud-native tools, microservices, CLIs and infrastructure components. Its strengths are simplicity, fast compilation and built-in concurrency – yet goroutines, channels, context handling and error handling all demand discipline. AI coding assistants and agents suit Go particularly well: the compiler, go vet and the race detector provide fast, unambiguous feedback that agents can iterate on. Typical tasks include table-driven tests, gRPC and HTTP handlers generated from specifications, tracking down race conditions or goroutine leaks and refactoring towards clean interfaces. This course shows you how to get idiomatic Go from AI and how to verify the results with Go’s own tooling.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Go developers in back-end and platform teams
  • Developers of cloud-native tools, operators and CLIs
  • Site reliability engineers maintaining their own Go code
  • Software architects designing high-performance microservices

Typical challenges we address

  • Subtle concurrency bugs such as race conditions, deadlocks and goroutine leaks
  • Repetitive boilerplate in error handling, handlers and tests
  • Inconsistent project layouts and interface boundaries in growing codebases
  • Difficult performance analysis of memory allocations and latency

What you will be able to do

  • You build a complete RAG pipeline from document preparation to answers with citations.
  • You choose embedding model, chunking strategy and vector database on solid grounds.
  • You improve retrieval quality with hybrid search, reranking and metadata filters.
  • You measure application quality with systematic evaluations and detect regressions.
  • Have AI agents produce idiomatic Go code, tests and services that follow Go conventions.
  • Use AI deliberately to analyse concurrency and performance problems.
  • Verify generated code with the compiler, go vet, the race detector, linting and benchmarks.

Course outline

Day 1

Module 1 Foundations of RAG and LLM applications
  • Anatomy of a RAG pipeline: ingestion, indexing, retrieval, generation
  • Understanding embeddings: semantic similarity, dimensions, model selection
  • Weighing RAG against fine-tuning and long context windows
  • Structured outputs, citations and dealing with hallucinations
Module 2 Document preparation and chunking
  • Reliably extracting PDFs, Office documents, HTML and tables
  • Chunking strategies: fixed size, structure-aware, semantic, with overlap
  • Enriching metadata and generating contextual summaries per chunk
  • Incremental updates and deletion of documents in the index
Module 3 Vector databases and retrieval strategies
  • Comparing vector databases and extensions such as pgvector, Qdrant, Weaviate or OpenSearch
  • Hybrid search combining vector and full-text search with BM25
  • Reranking, metadata filtering and query rewriting
  • Agentic retrieval: letting the model decide on search steps and tools
Module 4 Evaluation and quality assurance
  • Building and maintaining test sets from real user questions
  • Retrieval metrics such as recall and precision, and answer metrics such as faithfulness
  • Using and calibrating LLM-as-a-judge sensibly
  • Regression testing when chunking, model or prompt changes

Day 2

Module 5 Operations, cost and data protection
  • Prompt caching, batch processing and model choice to cut costs
  • Optimising latency and streaming responses
  • Document-level permissions, tenant isolation and protection against prompt injection
  • Data protection: data residency, processing agreements, deletion policies, GDPR and the EU AI Act
Module 6 Practice lab: Go
  • Writing table-driven tests
  • Implementing gRPC and HTTP services
  • Finding concurrency bugs
  • Idiomatic refactoring
  • Profiling performance
  • Building CLIs and tools
Module 7 Data protection, quality & transfer to daily work
  • Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Writing table-driven tests

The agent generates idiomatic table-driven tests with subtests, test helpers and fakes for interfaces, runs go test with the race detector and adds missing edge cases until coverage is where you want it.

Implementing gRPC and HTTP services

Protobuf definitions or OpenAPI specs become handlers, middleware for logging and authentication, context propagation with timeouts and clean error returns. Integration tests exercise the endpoints against realistic requests and failure cases.

Finding concurrency bugs

The AI analyses race detector output, goroutine dumps and code using channels or mutexes, explains the cause of deadlocks or leaks and proposes fixes using errgroup or context cancellation.

Idiomatic refactoring

Large packages are untangled, small consumer-side interfaces introduced, errors wrapped and checked with errors.Is, and code aligned with golangci-lint rules. Existing tests keep behaviour safe throughout.

Profiling performance

pprof profiles and benchmark results are interpreted, hotspots and unnecessary allocations identified, and optimisations backed by before-and-after benchmarks. Improvements become measurable rather than merely assumed.

Building CLIs and tools

The assistant creates command-line tools with Cobra or the standard library, including configuration, structured logging, cross-compilation and release automation. Tests, help texts and clear command documentation are included.

Possible hands-on projects

Health check aggregator

A Go service that polls the health endpoints of many services concurrently, merges the results and exposes them as JSON and Prometheus metrics.

Log shipper CLI

A lean command-line tool that tails log files, filters entries and ships them in batches to an HTTP endpoint – with backoff and graceful shutdown.

Kubernetes resource report

A CLI built on client-go that reads resource requests and limits across all deployments and presents anomalies in a clear table.

Prerequisites

Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs. Hands-on Go experience including goroutines, interfaces and modules, plus a working knowledge of Git and containers.

Technical requirements

A laptop with Python, Git, Docker and a code editor, plus API access to at least one LLM and embedding provider. Credentials for the exercises can be provided if needed.

Data protection & compliance

Go tooling often runs deep in the infrastructure, so keep kubeconfigs, tokens and certificates strictly out of an AI assistant’s context. Check new modules for provenance, licence and known vulnerabilities, for instance with govulncheck, and record dependencies in an SBOM as required under the Cyber Resilience Act for products with digital elements.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Stuttgart Request
  • Thu, 7 January 2027 – Fri, 8 January 2027 Hanover Request
  • Tue, 19 January 2027 – Wed, 20 January 2027 Online Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Berlin Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Online Request

Frequently asked questions

When is RAG better than fine-tuning?

RAG is the right choice when a model needs access to current, extensive or frequently changing knowledge and should cite its sources. Fine-tuning mainly changes style, format or specialised behaviour. In many projects RAG is the faster, cheaper and more controllable route, sometimes complemented by fine-tuning for niche tasks.

Which vector database should we use?

That depends on data volume, existing infrastructure, filtering needs and operating model. If you already run PostgreSQL, pgvector often takes you a long way, while specialised systems shine with large datasets and hybrid search. In the course you compare several options hands-on and derive criteria for your own decision.

How can RAG be run in a GDPR-compliant way?

Key factors are choosing providers with suitable data residency and a data processing agreement, enforcing document-level permissions, having a deletion policy for the index and logs, and minimising personal data. For highly sensitive data, self-hosted models are also an option. We work through these choices using realistic architectures.

Does AI really write idiomatic Go?

With good context, yes. Without guidance, models sometimes drift towards patterns from other languages, such as large interfaces or excessive abstraction. On the course you capture your Go conventions, linter configuration and example code so the assistant favours simple, idiomatic solutions, and you verify the output with golangci-lint.

Can AI reliably detect race conditions?

AI is a powerful analysis aid but no substitute for the race detector. The combination works best: the agent runs tests with the race detector, interprets the reports and proposes fixes. You learn to set up that loop and to critically assess any suggested synchronisation changes before accepting them.

How much does “RAG & LLM Applications for Go Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

More courses for Go Developers

More RAG & LLM Applications courses

All 29 RAG & LLM Applications courses

RAG & LLM Applications · MunichRAG & LLM Applications · SalzburgRAG & LLM Applications · BerlinRAG & LLM Applications · AugsburgRAG & LLM Applications · HanoverRAG & LLM Applications · BremenRAG & LLM Applications · StuttgartRAG & LLM Applications · KaiserslauternRAG & LLM Applications · Online