Training course · RAG & LLM Applications · Salesforce Development

RAG & LLM Applications for Salesforce 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 Salesforce Developers” is designed for roles such as Salesforce developers working with Apex and Lightning Web Components, Salesforce architects and technical leads and Salesforce administrators who also develop and shows step by step how to use RAG & LLM Applications productively, safely and transparently in Salesforce Development – from the fundamentals to advanced workflows.

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.

Salesforce orgs grow over the years into complex platforms of Apex classes, triggers, flows, Lightning Web Components and integrations. AI coding assistants and agents help Salesforce developers write Apex and LWC faster, generate test classes with meaningful coverage, account for governor limits early and move sprawling trigger logic into clean frameworks. They support SOQL optimisation, metadata analysis and the migration from Process Builder and workflow rules to Flow. Because Salesforce code must be multi-tenant aware, bulkified and security-conscious, AI needs targeted context and clear rules. In this course you learn to combine AI tools with Salesforce CLI, scratch orgs and your DevOps pipeline while reliably respecting sharing rules, field-level security and platform limits.

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?

  • Salesforce developers working with Apex and Lightning Web Components
  • Salesforce architects and technical leads
  • Salesforce administrators who also develop
  • Integration developers building Salesforce interfaces
  • Salesforce consultancies and partner teams

Typical challenges we address

  • Governor limits, bulkification and trigger recursion cause hard-to-reproduce errors
  • Mandatory test coverage consumes a lot of time writing test classes
  • Mature orgs with overlapping triggers, flows and processes and no documentation
  • Security requirements such as CRUD and FLS checks and sharing are often forgotten in code

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.
  • Develop Apex and Lightning Web Components with AI support that are bulkified, secure and within limits.
  • Generate meaningful test classes and analyse org metadata systematically with AI agents.
  • Integrate AI tools with Salesforce CLI, scratch orgs and your deployment pipeline.

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: Salesforce Development
  • Writing bulkified Apex
  • Generating test classes
  • Building Lightning Web Components
  • Optimising SOQL and performance
  • Analysing org metadata
  • Preparing for security review
Module 7 Data protection, quality & transfer to daily work
  • Salesforce orgs hold customer and sales data: you learn to share only metadata and anonymised samples with AI services, protect credentials and connected app secrets, implement GDPR requirements for data retention and clarify licensing questions around generated code in AppExchange packages.
  • 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 bulkified Apex

Generate trigger handlers, service classes and batch or queueable jobs that keep SOQL and DML out of loops, respect governor limits and apply patterns such as trigger frameworks consistently.

Generating test classes

Create meaningful Apex tests with test data factories, positive and negative cases, bulk scenarios and assertions that verify real logic rather than just coverage, including mocks for callouts.

Building Lightning Web Components

Develop LWC components with wire adapters, Apex controllers, Lightning Data Service and Jest tests, and migrate existing Aura components step by step. Accessibility and loading states are covered too.

Optimising SOQL and performance

Write selective queries, interpret Query Plan results, spot large data volume issues and propose alternatives such as custom indexes, skinny tables or asynchronous processing. This prevents timeouts as data grows.

Analysing org metadata

Evaluate retrieved metadata to document automations per object, dependencies between fields, flows and Apex, and unused components, and plan clean-up work. The result is finally up-to-date documentation.

Preparing for security review

Examine Apex and LWC for missing CRUD and FLS checks, SOQL injection, unsafe sharing settings and cross-site scripting, and implement fixes using user mode and stripInaccessible.

Possible hands-on projects

Automation map

A script that reads an org’s metadata and shows every trigger, flow and validation rule per object, including execution order, in one overview.

Test data factory generator

A tool that produces a reusable Apex factory from object and field definitions, with required fields and valid picklist values.

LWC dashboard component

A Lightning Web Component that aggregates an account’s key figures from several objects and displays them with filters and drill-down.

Prerequisites

Solid programming skills in Python (or TypeScript as an alternative), basic knowledge of databases and REST APIs, and some experience with LLM APIs. You already develop in Apex or Lightning Web Components and have a working knowledge of the Salesforce data model, governor limits and Salesforce CLI.

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

Salesforce orgs hold customer and sales data: you learn to share only metadata and anonymised samples with AI services, protect credentials and connected app secrets, implement GDPR requirements for data retention and clarify licensing questions around generated code in AppExchange packages.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Tue, 8 December 2026 – Wed, 9 December 2026 Salzburg Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Bremen Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Online Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Augsburg 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.

How well do general-purpose AI assistants know Apex and Salesforce specifics?

Current models handle Apex syntax and common patterns well. Weaknesses show up around governor limits, bulkification and org-specific objects. With rule files covering your trigger framework, naming conventions and security standards, plus retrieved metadata as context, results become far more reliable. In the course you build exactly this context for your org.

Should an AI agent deploy straight into our Salesforce org?

We recommend letting agents work in scratch orgs or sandboxes and promoting changes to higher environments through version control and your deployment pipeline. That preserves reviews, test runs and approvals. Production orgs should be reachable by agents read-only at most, using a restricted user. You practise this way of working hands-on in the course.

How much does “RAG & LLM Applications for Salesforce 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.

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