AI GLOSSARY

RAG

Retrieval-Augmented Generation combines AI language models with your company's own knowledge. This enables you to provide fact-based, up-to-date, and GDPR-compliant answers drawn from your documents and data sources.

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Components
of a good prompt

Techniques
in the toolbox

Approaches
PE vs. RAG vs. Fine-Tuning

Best Practices
for Effective Prompts

Why RAG Is the Key to In-House AI

For companies—especially small and medium-sized businesses—RAG is the fastest way to combine generative AI with their own knowledge. It’s fact-based, GDPR-compliant, and requires no expensive model training. RAG replaces hallucinations with verified answers from your sources.

Fact-Based Answers

AI responds based on your own documents rather than on model knowledge. Hallucinations are drastically reduced.

GDPR-compliant

Sensitive data remains within your infrastructure. Models are not used in third-party training.

Always Up-to-Date

New content is available immediately. No retraining is necessary when information changes.

Verifiable Sources

Every answer is backed by references. Verifiable, citable, and audit-proof.

No fine-tuning

No expensive model adjustments. First productive results in weeks, not months.

Scalable

From 100 to 100,000 documents: RAG architectures grow along with your knowledge base.

What is RAG?

Retrieval-Augmented Generation (RAG) is an AI method that supplements language models with external, company-specific knowledge. Instead of generating responses based solely on the trained model, the system searches your data sources for relevant content at runtime and provides it to the model as context.

Traditional large language models like GPT, Gemini, or Claude only know what they’ve been trained on. They lack access to internal manuals, up-to-date prices, ongoing projects, or GDPR-relevant customer data. RAG closes this gap.

At prodot, we implement end-to-end RAG systems—from data preparation through vector databases and retrieval to production monitoring. Learn more on our RAG Consulting & Implementation page.

Naive RAG

Direct retrieval followed by generation. A solid starting point for structured knowledge bases.

Advanced RAG

Query rewriting, reranking, and filtering significantly improve the quality of search results.

Agentic RAG

AI agents dynamically adjust the retrieval strategy and data sources based on the question.

Graph RAG

A knowledge graph structures the retrieval framework for linked, precise answers.

Hybrid Search

A combination of semantic vector search and traditional keyword search for maximum search result quality.

Multimodal RAG

Search across text, images, tables, and videos—for complex knowledge bases.

Corrective RAG

Self-check with a new search if the retrieval result is uncertain. More robust against missing results.

Cache-Augmented Generation

Large context windows are replacing traditional retrieval methods for compact, static knowledge bases.

Best Practices for RAG Implementations

In production RAG projects, these principles determine success or frustration:

Data quality first: A poor-quality document remains poor even after chunking. It’s worth cleaning up the data before each embedding.
Test your chunking strategy: Chunks that are too small break up the context; chunks that are too large dilute search results. Optimize iteratively.
Include metadata: Source, date, author, permissions—essential for filtering, citability, and access control.
Evaluate retrieval: Before you optimize the LLM, check whether the retriever is actually finding the right chunks.
Show sources: Users should be able to see where each answer comes from. This builds trust and helps catch errors early.
Incorporate a feedback loop: Thumbs up/down, comments, reports—every piece of feedback improves the system.

Approach 1

Prompt Engineering

Controlling a model solely through the input. Ideal for simple, knowledge-free tasks and as a complement to RAG.

Effort: Low

Approach 2

RAG

Retrieval-Augmented Generation loads up-to-date corporate knowledge into the prompt at runtime. The standard for fact-based, GDPR-compliant AI.

Effort: Medium

Approach 3

Fine-Tuning

Retraining the model with your own data. Useful for very specific tasks or when RAG alone is not sufficient.

Effort: High

Common Mistakes in RAG Projects

Many RAG projects fail not because of the model, but because of avoidable fundamental errors:

Naive default architecture: Without chunking optimization and reranking, results remain mediocre.
No Retrieval Testing: If you don’t measure whether relevant hits are found, you’re optimizing in the wrong place.
Forgotten access controls: Users see answers from documents they aren’t authorized to access—a data protection disaster.
Lumping all documents together: Without filters for recency, language, and subject area, the retriever mixes together disparate results.
No source citations: Answers without references cannot be verified and quickly lose credibility.
Prompt injection ignored: Without guardrails, manipulated documents can hijack the system’s behavior.

Use Cases in Small and Medium-Sized Businesses

Your contact person

Katja Kammilla

sales@prodot.de

0203 3965080

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Implementing RAG in Your Company

In a free initial consultation, we’ll review your use case: What data, which users, and what benefits? You’ll receive an initial assessment of feasibility, effort required, and the appropriate architecture.

As an AI partner for small and medium-sized businesses, we take RAG from prototype to reliable production—offering consulting, implementation, and managed services all under one roof. Learn more on our RAG Consulting & Implementation page.

RAG