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method#Artificial Intelligence#Machine Learning#Architecture#Data

RAG Implementation

A practical guide for implementing Retrieval-Augmented Generation (RAG). Describes architecture patterns, data flows, and evaluation criteria for knowledge-grounded generative AI systems.

Retrieval-Augmented Generation (RAG) is a method that augments generative models with external retrieval of documents to ground responses in factual knowledge.
Emerging
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Vector databases (e.g., Milvus, FAISS, Pinecone)Model serving platforms (e.g., Triton, Hugging Face Inference)Observability tools for latency and error analysis

Principles & goals

Separation of retrieval and generation for better traceability.Prioritize source verification and provenance-based responses.Iterative evaluation with human feedback for quality control.
Build
Team, Domain

Use cases & scenarios

Compromises

  • Uncontrolled disclosure of sensitive information from sources.
  • Over-reliance on unverified retrieval hits.
  • Cost increases due to storage and query load for indexes.
  • Link sources with provenance and disclose transparently.
  • Adjust index refresh interval to data characteristics.
  • Plan human-in-the-loop validation for critical answers.

I/O & resources

  • Source corpus (documents, KB, logs)
  • Embedding models and indexing pipeline
  • Generative model and configuration parameters
  • Generated, source-referenced answers
  • Relevance metrics and audit logs
  • Updated indices and versioned artifacts

Description

Retrieval-Augmented Generation (RAG) is a method that augments generative models with external retrieval of documents to ground responses in factual knowledge. It combines a retriever and a generator to improve accuracy and context-awareness. This method guides architecture, data pipelines, and evaluation for knowledge-intensive applications.

✔

  • Improved factuality compared to purely generative models.
  • Flexible knowledge updates via index updates.
  • Increased context relevance for domain-specific queries.

✖

  • Dependence on index quality and coverage of data sources.
  • Latency can be high due to retrieval steps.
  • Erroneous or contradictory sources can cause inconsistent answers.

  • Factuality Rate

    Share of answers supported by verifiable sources.

  • Retrieval Precision@k

    Precision of retrieved hits within the top-k results.

  • End-to-end latency

    Total time from request to response delivery in production.

Facebook AI research paper (RAG)

Original publication describing RAG as a retriever-generator combination.

Hugging Face Transformers RAG integration

Practical implementation and example code for RAG in Transformers.

Enterprise knowledge assistant pilot

Case study: support bot combining internal policies and documents via RAG.

1

Define target use cases and success criteria.

2

Assess, prepare and index data sources.

3

Choose retriever architecture and train/select embeddings.

4

Integrate generator model and develop prompt/response strategies.

5

Introduce monitoring, evaluation and feedback loops.

⚠️ Technical debt & bottlenecks

  • Unstructured indexes without partitioning strategy.
  • Ad-hoc retrieval tuning without test coverage.
  • Hardcoded prompts in application code.
indexingretrieval-latencystorage-costs
  • Use of confidential documents in index without masking.
  • Producing recommendations without quality checks.
  • Using stale indexes for critical decisions.
  • Overestimating generator capabilities with unreliable hits.
  • Lack of metrics to measure factuality.
  • Complex consistency issues across multiple sources.
Understanding of information retrieval and vectorization.Knowledge of prompt engineering and evaluation metrics.Operational skills for deployment, scaling and monitoring.
Answer accuracy and traceabilityLatency and scalability requirementsData quality and governance
  • • Legal requirements for data access and storage.
  • • Limited quality or coverage of available sources.
  • • Operational budget for index and inference infrastructure.