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concept#Artificial Intelligence#Data#Architecture#Software Engineering

Retrieval-Augmented Generation (RAG)

Concept combining information retrieval with generative language models to provide more factual and up-to-date responses.

Retrieval-Augmented Generation (RAG) combines external information retrieval with large language models to produce more factual and up-to-date responses.
Emerging
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Vector databases (e.g., FAISS, Milvus, Pinecone)Search and indexing pipelinesLLM services or on-premise models

Principles & goals

Separation of retrieval and generationSource citation and explainabilityIterative evaluation and monitoring
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Hallucinations still possible with poor retrieval
  • Confidentiality concerns with sensitive sources
  • Complexity in permissions and governance models
  • Always attach source citations and use verifiable quotes.
  • Regularly retrain or reindex retrieval models with fresh data.
  • Iteratively refine prompt templates and ranker metrics.

I/O & resources

  • Document corpus or data sources (indexable)
  • Embedding models or feature extractors
  • Large language model / generative model
  • Generated answers with source citations
  • Ranked retrieval sets and scores
  • Logging and evaluation metrics for monitoring

Description

Retrieval-Augmented Generation (RAG) combines external information retrieval with large language models to produce more factual and up-to-date responses. The concept integrates search, indexing and reranking components with generative models and defines interfaces, evaluation criteria and governance for knowledge-intensive applications. RAG addresses answer accuracy and timeliness.

✔

  • Improved factuality via external sources
  • Freshness without full model retraining
  • Better control over answer sources

✖

  • Dependence on index quality and coverage
  • Risk of inconsistent source integration
  • Latency introduced by retrieval steps

  • Answer accuracy (Factuality)

    Share of answers supported by verifiable sources.

  • Retrieval relevance (Recall@K)

    Percentage of relevant documents within top-K results.

  • End-to-end latency

    Time from request to final answer including retrieval and generation.

Knowledge-backed Chatbots

Chatbot uses internal document index and RAG for precise answer generation.

Expert Research Assistance

RAG assists analysts with aggregated, source-based answers from multiple repositories.

Combination with Retrieval-as-a-Service

Integration of external vector databases to improve retrieval quality.

1

Analyze corpus and identify relevant sources.

2

Set up indexing and embedding pipeline.

3

Integrate retrieval component and train/configure ranker.

4

Implement LLM integration, run tests and set up monitoring.

⚠️ Technical debt & bottlenecks

  • Monolithic indices without partitioning cause scaling issues
  • Hardcoded prompts and missing test suites for answers
  • Insufficient data versioning in knowledge sources
Vector indexingNetwork and latency pathsQuality of metadata
  • Exposing sensitive internal content via generated answers
  • Using outdated indices without reindexing
  • Replacing human review in safety-critical answers
  • Underestimating costs for embedding generation and storage
  • Difficulties in source attribution management
  • Lack of monitoring for drift in retrieval performance
Information retrieval and indexing skillsPrompt engineering and LLM understandingDevOps for scaling and monitoring
Indexing quality and retrieval accuracyModel capacity and prompt designSecurity, access control and privacy
  • • Limited index size or cost constraints
  • • Compliance requirements for source data
  • • Compute and storage needs for embedding generation