What can WFGY ProblemMap do?

rag pipeline failure mode identification, retrieval quality failure detection guidance, llm hallucination and generation failure detection guidance, latency and performance failure detection guidance, data quality and preprocessing failure detection guidance, model and embedding failure detection guidance, integration and api failure detection guidance, pre-deployment production readiness validation, team debugging framework standardization

WFGY ProblemMap

RepositoryFree

MIT-licensed checklist of 16 common RAG / LLM pipeline failure modes, used as a practical debugging...

Best for:Engineering teams actively building RAG systems or LLM pipelines who need a structured debugging framework to validate production readiness before launch.

/ 100

9 capabilities

Capabilities9 decomposed

rag pipeline failure mode identification

Medium confidence

Provides a structured checklist of 16 common failure modes across RAG (Retrieval-Augmented Generation) systems, enabling engineers to systematically identify potential breaking points in their retrieval and generation pipeline.

Solves for

I need to know what can go wrong in my RAG system before it hits productionI want a checklist to validate my RAG pipeline is production-readyI'm debugging a RAG system and need to know what failure modes to investigate

Best for

Backend engineers building RAG systems

ML engineers deploying LLM pipelines

DevOps teams responsible for LLM system reliability

Requires

Understanding of RAG architecture

Familiarity with LLM pipeline components

Manual testing and validation process

Limitations

Requires manual review—no automated detection of failures

Does not provide diagnostic tools or scripts to detect failures at runtime

Checklist is static and requires human interpretation of applicability

retrieval quality failure detection guidance

Medium confidence

Identifies failure modes specific to the retrieval component of RAG systems, such as poor document ranking, semantic mismatch, or index corruption, helping engineers diagnose why relevant context isn't being retrieved.

Solves for

My RAG system is returning irrelevant documents—what should I check?I need to validate that my retrieval component is working correctlyI want to understand common retrieval failure patterns

Best for

Engineers optimizing vector search and retrieval

Teams debugging low retrieval accuracy

RAG system architects

Requires

Access to retrieval logs or results

Understanding of your document corpus and embedding model

Limitations

Does not include metrics or thresholds for acceptable retrieval quality

No automated retrieval quality scoring or monitoring

Requires manual evaluation of retrieved documents

llm hallucination and generation failure detection guidance

Medium confidence

Outlines failure modes in the generation phase where LLMs produce incorrect, fabricated, or incoherent outputs despite receiving relevant context, helping teams identify why their model outputs are unreliable.

Solves for

My LLM is generating false information even with correct context—what's wrong?I need to validate that my generation pipeline isn't producing hallucinationsI want to understand why my model outputs are inconsistent or inaccurate

Best for

ML engineers tuning LLM behavior

Teams building fact-checking mechanisms

Product teams concerned with output quality

Requires

Sample LLM outputs to evaluate

Ground truth or reference data for comparison

Limitations

Does not provide automated hallucination detection

No guidance on prompt engineering or model selection trade-offs

Requires manual evaluation of generated outputs

latency and performance failure detection guidance

Medium confidence

Identifies failure modes related to slow response times, timeouts, and resource bottlenecks in RAG/LLM pipelines, helping teams diagnose performance degradation before it impacts users.

Solves for

My RAG system is too slow—where is the bottleneck?I need to validate that my pipeline meets latency requirementsI want to understand common performance failure patterns in LLM systems

Best for

DevOps and infrastructure engineers

Performance optimization teams

Product teams with SLA requirements

Requires

Access to system logs and timing data

Understanding of your infrastructure and model serving setup

Limitations

Does not include performance benchmarking tools

No automated latency monitoring or alerting setup

Requires manual profiling and testing

data quality and preprocessing failure detection guidance

Medium confidence

Highlights failure modes in data ingestion, cleaning, and preprocessing stages that can corrupt embeddings, introduce noise, or create misalignment between documents and queries in RAG systems.

Solves for

My embeddings seem wrong—could it be a data quality issue?I need to validate that my document preprocessing is correctI want to understand how data quality impacts RAG performance

Best for

Data engineers managing document pipelines

Teams building data validation frameworks

RAG system architects

Requires

Access to raw and processed data samples

Understanding of your data schema and preprocessing logic

Limitations

Does not provide data quality scoring or validation tools

No automated data profiling or anomaly detection

Requires manual inspection of processed data

model and embedding failure detection guidance

Medium confidence

Identifies failure modes related to embedding model selection, version mismatches, and model degradation that can cause semantic drift or incompatibility between retrieval and generation components.

Solves for

My embeddings don't match my queries—is it a model issue?I need to validate that my embedding and LLM models are compatibleI want to understand how model selection impacts RAG reliability

Best for

ML engineers selecting and managing models

Teams managing model versioning and updates

RAG system architects

Requires

Access to model metadata and versions

Understanding of embedding and LLM model characteristics

Limitations

Does not provide model evaluation or benchmarking

No automated model compatibility checking

Requires manual testing of model combinations

integration and api failure detection guidance

Medium confidence

Outlines failure modes in how RAG/LLM components integrate with external services, APIs, and databases, such as connection failures, rate limiting, or data format mismatches.

Solves for

My RAG system is failing to connect to external services—what should I check?I need to validate that my API integrations are robustI want to understand common integration failure patterns

Best for

Backend engineers managing integrations

DevOps teams responsible for system reliability

Teams building resilient RAG systems

Requires

Access to integration logs and error messages

Understanding of external service contracts and APIs

Limitations

Does not provide integration testing frameworks

No automated failure injection or chaos testing

Requires manual integration testing

pre-deployment production readiness validation

Medium confidence

Provides a comprehensive checklist to validate that a RAG/LLM system is ready for production deployment by systematically checking all 16 failure modes across the entire pipeline.

Solves for

I'm about to deploy my RAG system—is it production-ready?I need a final sanity check before launching to usersI want to ensure I haven't missed any critical failure modes

Best for

Engineering teams preparing for production launch

Release managers and QA teams

Technical leads responsible for system reliability

Requires

Complete RAG/LLM system implementation

Access to staging or test environment

Team familiarity with all system components

Limitations

Checklist is manual—requires human review and sign-off

Does not automate validation or generate compliance reports

Effectiveness depends on team's understanding of each failure mode

team debugging framework standardization

Medium confidence

Enables teams to adopt a shared, standardized mental model for debugging RAG/LLM failures by providing a common vocabulary and structured approach to failure investigation.

Solves for

I want my team to have a consistent approach to debugging RAG issuesI need to standardize how we investigate production incidentsI want to reduce debugging time by using a proven framework

Best for

Engineering teams building RAG systems

Organizations scaling LLM deployments

Teams with distributed or rotating on-call responsibilities

Requires

Team alignment on debugging methodology

Documentation and training materials

Limitations

Requires team training and adoption effort

Does not include runbooks or incident response automation

Effectiveness depends on team discipline in following the checklist

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with WFGY ProblemMap, ranked by overlap. Discovered automatically through the match graph.

Product22

Cleanlab

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automated hallucination remediation with suggested correctionsllm hallucination detection via confidence scoringreal-time hallucination monitoring and alertingmulti-llm hallucination comparison and consensus scoring

4 shared capabilities

Product31

Cleanlab

Detect and remediate hallucinations in any LLM...

hallucination detection and flaggingproduction llm application quality monitoringhallucination remediation strategy selection

3 shared capabilities

Product33

Aporia

Real-time AI security and compliance for robust, reliable...

llm-specific hallucination detectionreal-time model output anomaly detection

2 shared capabilities

Platform40

Galileo

AI evaluation platform with hallucination detection and guardrails.

hallucination detection and guardrail enforcementfailure mode analysis and pattern detection

2 shared capabilities

Platform40

Galileo Observe

AI evaluation platform with automated hallucination detection and RAG metrics.

automated hallucination detection in llm outputs

1 shared capability

Product32

Monitaur

AI governance platform enhancing compliance, risk management, and...

hallucination-detection-and-flagging

1 shared capability

Best For

✓Backend engineers building RAG systems
✓ML engineers deploying LLM pipelines
✓DevOps teams responsible for LLM system reliability
✓Engineers optimizing vector search and retrieval
✓Teams debugging low retrieval accuracy
✓RAG system architects
✓ML engineers tuning LLM behavior
✓Teams building fact-checking mechanisms

Known Limitations

⚠Requires manual review—no automated detection of failures
⚠Does not provide diagnostic tools or scripts to detect failures at runtime
⚠Checklist is static and requires human interpretation of applicability
⚠Does not include metrics or thresholds for acceptable retrieval quality
⚠No automated retrieval quality scoring or monitoring
⚠Requires manual evaluation of retrieved documents

Requirements

Understanding of RAG architectureFamiliarity with LLM pipeline componentsManual testing and validation processAccess to retrieval logs or resultsUnderstanding of your document corpus and embedding modelSample LLM outputs to evaluateGround truth or reference data for comparisonAccess to system logs and timing data

Input / Output

Accepts: Mental model of your RAG/LLM system, Query examples, Retrieved document samples, Generated text samples, Context provided to the model, Latency measurements, System resource metrics, Raw documents, Processed/embedded documents, Model specifications, Test queries and documents, Integration test results, API error logs, System architecture documentation, Test results and metrics, Team feedback and incident reports

Produces: Checklist items to validate, Debugging priorities, Retrieval failure mode checklist, Debugging hypotheses, Hallucination failure mode checklist, Output quality assessment framework, Performance failure mode checklist, Bottleneck identification framework, Data quality failure mode checklist, Data validation priorities, Model failure mode checklist, Model compatibility assessment, Integration failure mode checklist, Resilience improvement priorities, Production readiness checklist, Go/no-go decision framework, Standardized debugging process, Incident investigation templates

UnfragileRank

Adoption15%(30% weight)

Quality55%(20% weight)

Ecosystem25%(15% weight)

Match Graph25%(30% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Repository

9 capabilities

Visit WFGY ProblemMap→

About

MIT-licensed checklist of 16 common RAG / LLM pipeline failure modes, used as a practical debugging guide

Unfragile Review

WFGY ProblemMap is a lean, MIT-licensed debugging checklist that maps 16 concrete failure modes across RAG and LLM pipelines—from retrieval quality to hallucination to latency issues. It's a practical reference tool for engineers shipping production systems rather than a comprehensive framework, making it most valuable as a pre-deployment sanity check.

Pros

+Covers the full RAG/LLM stack with specific, actionable failure modes rather than abstract principles
+MIT license and GitHub-based distribution removes friction for team adoption and CI/CD integration
+Lightweight checklist format forces discipline without overwhelming teams with academic depth

Cons

-No automation or tooling—it's a static reference document that requires manual cross-checking by developers
-Limited visibility into how widely adopted or battle-tested these 16 modes are across real production systems
-Lacks diagnostic scripts, instrumentation examples, or integration with monitoring tools to detect these failures at runtime

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Capabilities9 decomposed

rag pipeline failure mode identification

Medium confidence

Solves for

Best for

Backend engineers building RAG systems

ML engineers deploying LLM pipelines

DevOps teams responsible for LLM system reliability

Requires

Understanding of RAG architecture

Familiarity with LLM pipeline components

Manual testing and validation process

Limitations

Requires manual review—no automated detection of failures

Does not provide diagnostic tools or scripts to detect failures at runtime

Checklist is static and requires human interpretation of applicability

retrieval quality failure detection guidance

Medium confidence

Solves for

My RAG system is returning irrelevant documents—what should I check?I need to validate that my retrieval component is working correctlyI want to understand common retrieval failure patterns

Best for

Engineers optimizing vector search and retrieval

Teams debugging low retrieval accuracy

RAG system architects

Requires

Access to retrieval logs or results

Understanding of your document corpus and embedding model

Limitations

Does not include metrics or thresholds for acceptable retrieval quality

No automated retrieval quality scoring or monitoring

Requires manual evaluation of retrieved documents

llm hallucination and generation failure detection guidance

Medium confidence

Solves for

Best for

ML engineers tuning LLM behavior

Teams building fact-checking mechanisms

Product teams concerned with output quality

Requires

Sample LLM outputs to evaluate

Ground truth or reference data for comparison

Limitations

Does not provide automated hallucination detection

No guidance on prompt engineering or model selection trade-offs

Requires manual evaluation of generated outputs

latency and performance failure detection guidance

Medium confidence

Identifies failure modes related to slow response times, timeouts, and resource bottlenecks in RAG/LLM pipelines, helping teams diagnose performance degradation before it impacts users.

Solves for

My RAG system is too slow—where is the bottleneck?I need to validate that my pipeline meets latency requirementsI want to understand common performance failure patterns in LLM systems

Best for

DevOps and infrastructure engineers

Performance optimization teams

Product teams with SLA requirements

Requires

Access to system logs and timing data

Understanding of your infrastructure and model serving setup

Limitations

Does not include performance benchmarking tools

No automated latency monitoring or alerting setup

Requires manual profiling and testing

data quality and preprocessing failure detection guidance

Medium confidence

Highlights failure modes in data ingestion, cleaning, and preprocessing stages that can corrupt embeddings, introduce noise, or create misalignment between documents and queries in RAG systems.

Solves for

My embeddings seem wrong—could it be a data quality issue?I need to validate that my document preprocessing is correctI want to understand how data quality impacts RAG performance

Best for

Data engineers managing document pipelines

Teams building data validation frameworks

RAG system architects

Requires

Access to raw and processed data samples

Understanding of your data schema and preprocessing logic

Limitations

Does not provide data quality scoring or validation tools

No automated data profiling or anomaly detection

Requires manual inspection of processed data

model and embedding failure detection guidance

Medium confidence

Identifies failure modes related to embedding model selection, version mismatches, and model degradation that can cause semantic drift or incompatibility between retrieval and generation components.

Solves for

My embeddings don't match my queries—is it a model issue?I need to validate that my embedding and LLM models are compatibleI want to understand how model selection impacts RAG reliability

Best for

ML engineers selecting and managing models

Teams managing model versioning and updates

RAG system architects

Requires

Access to model metadata and versions

Understanding of embedding and LLM model characteristics

Limitations

Does not provide model evaluation or benchmarking

No automated model compatibility checking

Requires manual testing of model combinations

integration and api failure detection guidance

Medium confidence

Outlines failure modes in how RAG/LLM components integrate with external services, APIs, and databases, such as connection failures, rate limiting, or data format mismatches.

Solves for

My RAG system is failing to connect to external services—what should I check?I need to validate that my API integrations are robustI want to understand common integration failure patterns

Best for

Backend engineers managing integrations

DevOps teams responsible for system reliability

Teams building resilient RAG systems

Requires

Access to integration logs and error messages

Understanding of external service contracts and APIs

Limitations

Does not provide integration testing frameworks

No automated failure injection or chaos testing

Requires manual integration testing

pre-deployment production readiness validation

Medium confidence

Provides a comprehensive checklist to validate that a RAG/LLM system is ready for production deployment by systematically checking all 16 failure modes across the entire pipeline.

Solves for

I'm about to deploy my RAG system—is it production-ready?I need a final sanity check before launching to usersI want to ensure I haven't missed any critical failure modes

Best for

Engineering teams preparing for production launch

Release managers and QA teams

Technical leads responsible for system reliability

Requires

Complete RAG/LLM system implementation

Access to staging or test environment

Team familiarity with all system components

Limitations

Checklist is manual—requires human review and sign-off

Does not automate validation or generate compliance reports

Effectiveness depends on team's understanding of each failure mode

team debugging framework standardization

Medium confidence

Enables teams to adopt a shared, standardized mental model for debugging RAG/LLM failures by providing a common vocabulary and structured approach to failure investigation.

Solves for

I want my team to have a consistent approach to debugging RAG issuesI need to standardize how we investigate production incidentsI want to reduce debugging time by using a proven framework

Best for

Engineering teams building RAG systems

Organizations scaling LLM deployments

Teams with distributed or rotating on-call responsibilities

Requires

Team alignment on debugging methodology

Documentation and training materials

Limitations

Requires team training and adoption effort

Does not include runbooks or incident response automation

Effectiveness depends on team discipline in following the checklist

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Unfragile Review

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WFGY ProblemMap

Capabilities9 decomposed

rag pipeline failure mode identification

retrieval quality failure detection guidance

llm hallucination and generation failure detection guidance

latency and performance failure detection guidance

data quality and preprocessing failure detection guidance

model and embedding failure detection guidance

integration and api failure detection guidance

pre-deployment production readiness validation

team debugging framework standardization

Related Artifactssharing capabilities

Cleanlab

Cleanlab

Aporia

Galileo

Galileo Observe

Monitaur

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to WFGY ProblemMap

Are you the builder of WFGY ProblemMap?

Get the weekly brief

Data Sources

WFGY ProblemMap

Capabilities9 decomposed

rag pipeline failure mode identification

retrieval quality failure detection guidance

llm hallucination and generation failure detection guidance

latency and performance failure detection guidance

data quality and preprocessing failure detection guidance

model and embedding failure detection guidance

integration and api failure detection guidance

pre-deployment production readiness validation

team debugging framework standardization

Related Artifactssharing capabilities

Cleanlab

Cleanlab

Aporia

Galileo

Galileo Observe

Monitaur

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Unfragile Review

Pros

Cons

Categories

Alternatives to WFGY ProblemMap

Are you the builder of WFGY ProblemMap?

Get the weekly brief

Data Sources