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Naive RAG(NRAG)

Foundational "Retrieve-Read" framework following traditional indexing, retrieval, and generation process

Complexity: lowKnowledge Retrieval (RAG)

In 30 seconds

What: Retrieves top matching document passages via semantic search, concatenates them into the prompt, then generates an answer.
When to use: Straightforward factual questions over well-indexed documents where retrieval quality is high and no multi-step reasoning is needed.
Watch out: Retrieved passages may contradict each other or contain irrelevant noise, degrading answer quality without ranking or filtering.

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Foundational "Retrieve-Read" framework following traditional indexing, retrieval, and generation process

Simple retrieval pipeline
Direct passage integration
Basic semantic search
Sequential processing
Straightforward generation
Minimal preprocessing

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References

The papers, specifications, and repositories this pattern is based on.

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)arXiv:2004.04906
Retrieval Augmentation Reduces Hallucination in ConversationarXiv:2104.07567
Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)arXiv:2312.10997
A Comprehensive Survey of RAG: Evolution and Future Directions (Gupta et al., 2024)arXiv:2410.12837

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Patterns

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Design Patterns & Techniques

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📚

Naive RAG(NRAG)

Foundational "Retrieve-Read" framework following traditional indexing, retrieval, and generation process

Complexity: lowKnowledge Retrieval (RAG)

In 30 seconds

What: Retrieves top matching document passages via semantic search, concatenates them into the prompt, then generates an answer.
When to use: Straightforward factual questions over well-indexed documents where retrieval quality is high and no multi-step reasoning is needed.
Watch out: Retrieved passages may contradict each other or contain irrelevant noise, degrading answer quality without ranking or filtering.

Ask the AI expert about this pattern

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30-Second Overview

Pattern: Simple retrieve-then-read pipeline: query → retrieve documents → concatenate → generate

Why: Provides external knowledge access with minimal complexity - foundational approach established by Lewis et al. (2020)

Key Insight: Direct concatenation of top-k retrieved documents to query prompt - no optimization or post-processing

Quick Implementation

1Index:Create vector embeddings of knowledge base

2Retrieve:Find top-k relevant documents via similarity

3Concatenate:Append retrieved docs to query prompt

4Generate:Submit combined prompt to LLM

5Return:Output generated response directly

Example: query → vector_search → concat_context → llm_generate → response

Do's & Don'ts

✅Use semantic embeddings (e.g., sentence-transformers)

✅Implement basic chunking strategy (fixed size 512-1024 tokens)

✅Set clear top-k retrieval limits (typically 3-5 documents)

✅Include document metadata and source attribution

✅Use FAISS or similar for efficient vector search

❌Skip query preprocessing or normalization

❌Retrieve too many documents (causes context pollution)

❌Ignore relevance scoring thresholds

❌Concatenate without clear document boundaries

❌Expect sophisticated reasoning from basic approach

When to Use

Use When

• Simple Q&A over documents
• Proof-of-concept RAG systems
• Limited technical complexity allowed
• Small to medium knowledge bases
• Straightforward factual queries

Avoid When

• Complex multi-hop reasoning required
• High-accuracy critical applications
• Large-scale production systems
• Noisy or contradictory knowledge bases
• Real-time performance requirements

Key Metrics

Retrieval Accuracy

Relevant docs in top-k (Recall@k)

Answer Quality

BLEU/ROUGE scores vs ground truth

Response Time

End-to-end latency (retrieval + generation)

Context Utilization

% of retrieved context used in response

Hallucination Rate

% responses with unsupported claims

Source Attribution

% responses with correct source citations

Top Use Cases

Document Q&A: Simple factual questions over company documents or knowledge bases

FAQ Systems: Automated responses to frequently asked questions using existing documentation

Research Assistance: Basic information retrieval from academic papers or research collections

Customer Support: Level 1 support queries answerable from documentation and manuals

Educational Tools: Simple question-answering over textbooks and educational materials

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)

Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)

Retrieval Augmentation Reduces Hallucination in Conversation

Comprehensive Surveys

Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)

A Comprehensive Survey of RAG: Evolution and Future Directions (Gupta et al., 2024)

Retrieval-Augmented Generation for AI-Generated Content: A Survey (Li et al., 2024)

RAG and RAU: A Survey on Retrieval-Augmented Language Model (Zhao et al., 2024)

Implementation Resources

LangChain RAG Tutorial - Basic Implementation

Hugging Face RAG Documentation and Examples

OpenAI RAG Implementation Guide

LlamaIndex Basic RAG Pipeline Tutorial

Vector Database Solutions

FAISS - Facebook AI Similarity Search Library

Pinecone Vector Database Documentation

Weaviate Vector Search Engine

Chroma - Open-source Embedding Database

Embedding Models & Evaluation

Sentence-BERT: Sentence Embeddings using Siamese BERT (Reimers & Gurevych, 2019)

Text and Code Embeddings by Contrastive Pre-Training (OpenAI, 2022)

MTEB: Massive Text Embedding Benchmark (Muennighoff et al., 2022)

BGE: BAAI General Embedding Model Documentation

Evaluation Frameworks

RAGAS: Evaluation Framework for RAG Applications

TruLens: Evaluation and Tracking for LLM Applications

LangSmith: LLM Application Testing and Monitoring

DeepEval: Unit Testing for LLM Applications

Foundational "Retrieve-Read" framework following traditional indexing, retrieval, and generation process

Simple retrieval pipeline
Direct passage integration
Basic semantic search
Sequential processing
Straightforward generation
Minimal preprocessing

The Agent Architect

One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.

Weekly email, one-click unsubscribe. We only use your address to send the briefing.

References

The papers, specifications, and repositories this pattern is based on.

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., 2020)arXiv:2004.04906
Retrieval Augmentation Reduces Hallucination in ConversationarXiv:2104.07567
Retrieval-Augmented Generation for Large Language Models: A Survey (Gao et al., 2023)arXiv:2312.10997
A Comprehensive Survey of RAG: Evolution and Future Directions (Gupta et al., 2024)arXiv:2410.12837

From the engineer behind this catalog

Get your agent architecture reviewed

€750 instead of €1,500, one week, written report and walkthrough call, until 30 September

See the review

Loading patterns…

Naive RAG(NRAG)

In 30 seconds

Ask the AI expert about this pattern

Naive RAG: Overview

The Agent Architect

References

Get your agent architecture reviewed

Patterns

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Design Patterns & Techniques

Loading patterns…

Naive RAG(NRAG)

In 30 seconds

Ask the AI expert about this pattern

30-Second Overview

Quick Implementation

Do's & Don'ts

When to Use

Use When

Avoid When

Key Metrics

Top Use Cases

References & Further Reading

Foundational Papers

Comprehensive Surveys

Implementation Resources

Vector Database Solutions

Embedding Models & Evaluation

Evaluation Frameworks

Naive RAG: Overview

The Agent Architect

References

Get your agent architecture reviewed

Patterns

Design Patterns & Techniques

Loading patterns…

Design Patterns & Techniques

30-Second Overview

Quick Implementation

Do's & Don'ts

When to Use

Use When

Avoid When

Key Metrics

Top Use Cases

References & Further Reading

Foundational Papers

Comprehensive Surveys

Implementation Resources

Vector Database Solutions

Embedding Models & Evaluation

Evaluation Frameworks

Design Patterns & Techniques