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Building AI Features with Retrieval-Augmented Generation

A practical guide to connecting language models with project data using Retrieval-Augmented Generation (RAG), including access-control and evaluation considerations.

CODEEN TECHNOLOGIES
July 28, 2026
6 min read

From Chatbots to Data-Grounded AI Features

When connecting language models to application data, teams need to consider which sources can be retrieved, how responses are evaluated, and where human review is appropriate.

Retrieval-Augmented Generation (RAG) can ground responses in retrieved material and may reduce unsupported answers, but it does not eliminate hallucinations or guarantee data privacy.

RAG Design Considerations

1. Multi-Stage Chunking: Avoid uniform text splitting. Use document-aware chunking (hierarchical headers, table preservation) to maintain context boundaries.

2. Hybrid Search Indexing: Combine dense vector embeddings with sparse keyword search (BM25) to capture both semantic meaning and exact keyword references like product IDs.

3. Reranking Pipelines: Pass top retrieval candidates through specialized reranker models (e.g. Cohere Rerank) to filter out irrelevant context before prompting the LLM.

Access Controls and Evaluation

For applications with user-specific records, access checks should be part of the retrieval design so results can be limited to information the current user is authorized to see. Test these paths with representative roles and data before release.

#Artificial Intelligence#RAG#LLM#Python#Vector DB
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