Loading patterns…

🪞

Self-RAG(SRAG)

Self-reflective RAG that adaptively determines retrieval necessity and evaluates retrieval quality through reflection tokens

Complexity: highKnowledge Retrieval (RAG)

In 30 seconds

What: Agent decides whether to retrieve external knowledge, evaluates retrieved content quality, and assesses its own output before responding, using reflection tokens to guide these decisions.
When to use: Tasks requiring factual accuracy where the agent's confidence varies, or where retrieval cost matters and you want to skip unnecessary lookups.
Watch out: Reflection tokens add latency and cost; poor calibration causes the agent to retrieve when it shouldn't or skip retrieval when it should.

Ask the AI expert about this pattern

Opens the assistant with your question prefilled. You review it before sending.

Get this catalog as an MCP server Tune a prompt for this pattern

Self-reflective RAG that adaptively determines retrieval necessity and evaluates retrieval quality through reflection tokens

Retrieval necessity prediction
Retrieved content evaluation
Output quality assessment
Self-correction mechanisms
Reflection tokens
Adaptive retrieval strategies

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.

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (Asai et al., 2023)arXiv:2310.11511
SELF-REFINE: Iterative Refinement with Self-Feedback (Madaan et al., 2023)arXiv:2303.17651
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
Self-RAG Official Implementation: Reflection Token Training
Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)arXiv:2212.08073

From the engineer behind this catalog

Get your agent architecture reviewed

This page documents one pattern. Your system runs dozens, and most failures live in how they fit together. Have the whole design reviewed against the 288 patterns in this catalog: architecture, reliability, evaluation and cost, every finding mapped to the pattern that fixes it.

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

See the review

Patterns

Loading patterns…

Design Patterns & Techniques

Loading patterns…

🪞

Self-RAG(SRAG)

Self-reflective RAG that adaptively determines retrieval necessity and evaluates retrieval quality through reflection tokens

Complexity: highKnowledge Retrieval (RAG)

In 30 seconds

What: Agent decides whether to retrieve external knowledge, evaluates retrieved content quality, and assesses its own output before responding, using reflection tokens to guide these decisions.
When to use: Tasks requiring factual accuracy where the agent's confidence varies, or where retrieval cost matters and you want to skip unnecessary lookups.
Watch out: Reflection tokens add latency and cost; poor calibration causes the agent to retrieve when it shouldn't or skip retrieval when it should.

Ask the AI expert about this pattern

Opens the assistant with your question prefilled. You review it before sending.

Get this catalog as an MCP server Tune a prompt for this pattern

30-Second Overview

Pattern: RAG system with adaptive retrieval decisions and self-reflection using trained reflection tokens for quality assessment

Why: Improves factual accuracy and reduces hallucinations through iterative self-critique and selective knowledge retrieval

Key Insight: Reflection tokens ([Retrieve], [IsRel], [IsSup], [IsUse]) enable models to assess retrieval necessity and response quality

Quick Implementation

1Retrieval Gate:Assess whether external knowledge is needed for the query

2Retrieve & Rank:Fetch relevant passages and apply neural reranking

3Generate Draft:Create initial response using retrieved context

4Self-Critique:Use reflection tokens to evaluate response quality

5Refine Output:Iteratively improve based on self-assessment

Example: query → retrieval_gate → retrieve → generate → self_critique → [refine] → final_response

Do's & Don'ts

✅Train models with reflection tokens ([Retrieve], [IsRel], [IsSup], [IsUse])

✅Implement retrieval necessity prediction to avoid unnecessary context

✅Use calibrated confidence thresholds for triggering refinement

✅Enforce citation requirements with evidence grounding

✅Cache reflection outputs and retrieval results for efficiency

❌Allow unconstrained reflection tokens that become verbose

❌Skip validation of self-critique calibration against ground truth

❌Create infinite refinement loops without iteration limits

❌Rely solely on self-assessment without external validation

❌Ignore computational cost of multiple generation rounds

When to Use

Use When

• Factual accuracy and verifiability are critical requirements
• Domain expertise requires balancing parametric and retrieved knowledge
• Applications need confidence calibration and uncertainty quantification
• High-stakes decisions requiring explainable reasoning and citations
• Knowledge-intensive tasks in medical, legal, or scientific domains

Avoid When

• Simple queries where standard RAG provides sufficient accuracy
• Real-time applications with strict latency constraints
• Resource-constrained environments limiting multiple generation rounds
• Domains with insufficient training data for reliable self-critique
• Applications where citation overhead is unnecessary

Key Metrics

Answer Faithfulness

Factual accuracy and consistency with retrieved evidence

Reflection Calibration

Correlation between confidence scores and actual accuracy

Retrieval Precision

Proportion of retrieved passages that are genuinely useful

Citation Coverage

Percentage of claims supported by retrieved evidence

Refinement Effectiveness

Quality improvement through iterative self-correction

Computational Efficiency

Quality gains per additional token or retrieval call

Top Use Cases

Medical Q&A: Clinical decision support requiring high accuracy and evidence-based responses

Legal Research: Case law analysis with citation requirements and confidence assessment

Scientific Literature Review: Research synthesis with source attribution and uncertainty quantification

Financial Analysis: Investment research requiring balanced parametric and real-time market data

Educational Content: Academic tutoring with verified information and learning confidence tracking

References & Further Reading

Deepen your understanding with these curated resources

Foundational Papers & Self-RAG Research

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (Asai et al., 2023)

SELF-REFINE: Iterative Refinement with Self-Feedback (Madaan et al., 2023)

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

Reflection Tokens & Training Methods

Self-RAG Official Implementation: Reflection Token Training

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Training Language Models with Language Feedback at Scale (Scheurer et al., 2023)

Self-Consistency Improves Chain of Thought Reasoning (Wang et al., 2022)

Retrieval & Context Assessment

When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories (Mallen et al., 2023)

Active Retrieval Augmented Generation (Jiang et al., 2023)

Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering (Izacard & Grave, 2021)

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

Implementation Frameworks & Tools

LangChain Self-Query Retrieval and Reflection Chains

Haystack Retriever Documentation

Transformers Library: Self-RAG Model Integration

Evaluation & Calibration Methods

RAGAS: Automated Evaluation of RAG Applications

TruLens: RAG Triad evaluation

Calibrating Sequence Likelihood Improves Conditional Language Generation (Zhao et al., 2022)

Teaching Models to Express Their Uncertainty in Words (Lin et al., 2022)

Production Deployment & Optimization

LangSmith: Monitoring and Evaluation for Self-RAG Systems

OpenAI Function Calling for Self-Critique Implementation

Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)

Self-reflective RAG that adaptively determines retrieval necessity and evaluates retrieval quality through reflection tokens

Retrieval necessity prediction
Retrieved content evaluation
Output quality assessment
Self-correction mechanisms
Reflection tokens
Adaptive retrieval strategies

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.

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection (Asai et al., 2023)arXiv:2310.11511
SELF-REFINE: Iterative Refinement with Self-Feedback (Madaan et al., 2023)arXiv:2303.17651
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks (Lewis et al., 2020)arXiv:2005.11401
Self-RAG Official Implementation: Reflection Token Training
Constitutional AI: Harmlessness from AI Feedback (Bai et al., 2022)arXiv:2212.08073

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…

Self-RAG(SRAG)

In 30 seconds

Ask the AI expert about this pattern

Self-RAG: Overview

The Agent Architect

References

Get your agent architecture reviewed

Patterns

Loading patterns…

Design Patterns & Techniques

Loading patterns…

Self-RAG(SRAG)

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 & Self-RAG Research

Reflection Tokens & Training Methods

Retrieval & Context Assessment

Implementation Frameworks & Tools

Evaluation & Calibration Methods

Production Deployment & Optimization

Self-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 & Self-RAG Research

Reflection Tokens & Training Methods

Retrieval & Context Assessment

Implementation Frameworks & Tools

Evaluation & Calibration Methods

Production Deployment & Optimization

Design Patterns & Techniques