🤖 GenAI Troubleshooting Guide

Interactive guide for debugging Generative AI chatbots & RAG systems

📚 Scenario-Based Q&A

🔴 Chatbot giving incorrect answers?

Click to see troubleshooting steps

Steps:
1. Check prompt quality & clarity
2. Review retrieved documents (RAG)
3. Evaluate embedding quality
4. Assess chunking strategy
5. Check context window limits
6. Review model settings (temp, top_p)
7. Analyze evaluation logs

🐌 Slow response times?

Click to diagnose latency issues

Check:
1. Vector DB query performance
2. Embedding generation time
3. Model inference latency
4. Network/API bottlenecks
5. Batch size optimization
6. Caching strategies

🎭 Hallucinations in responses?

Click to reduce fabricated content

Solutions:
1. Lower temperature setting
2. Improve retrieval relevance
3. Add source citations
4. Use grounding techniques
5. Implement fact-checking
6. Fine-tune on domain data

📄 Poor document retrieval?

Click to improve RAG accuracy

Optimize:
1. Chunk size (512-1024 tokens)
2. Overlap between chunks
3. Embedding model choice
4. Hybrid search (semantic+keyword)
5. Reranking strategies
6. Metadata filtering

🔒 Context window overflow?

Click to handle token limits

Strategies:
1. Summarize long contexts
2. Use sliding window
3. Prioritize recent/relevant
4. Implement memory systems
5. Choose larger context models
6. Compress retrieved docs

🎯 Inconsistent outputs?

Click to stabilize responses

Fix:
1. Set temperature to 0
2. Use seed parameter
3. Standardize prompts
4. Implement output validation
5. Add few-shot examples
6. Use structured outputs

🔀 Troubleshooting Flowchart

🚨 Issue Detected

What type of problem?
↓

Is it accuracy-related?

Wrong answers, hallucinations, irrelevant responses
↓ Yes

1️⃣ Check Prompt Quality

Is the prompt clear, specific, and well-structured?
↓

2️⃣ Evaluate Retrieval (RAG)

Are the right documents being retrieved? Check similarity scores.
↓

3️⃣ Assess Embeddings

Is the embedding model appropriate for your domain?
↓

4️⃣ Review Chunking

Are chunks too small (missing context) or too large (noise)?
↓

5️⃣ Check Context Window

Is important info being truncated? Token count within limits?
↓

6️⃣ Tune Model Settings

Adjust temperature, top_p, frequency_penalty
↓

7️⃣ Analyze Logs

Review evaluation metrics, user feedback, error patterns
↓

✅ Issue Resolved?

If not, iterate or escalate

✅ Diagnostic Checklist

0 of 20 completed

📝 Prompt Engineering

🔍 RAG & Retrieval

🧮 Embeddings

✂️ Chunking Strategy

📏 Context Window

⚙️ Model Settings

🎯 Knowledge Quiz

1. Your chatbot is hallucinating facts. What should you check first?

2. What's the recommended chunk size for most RAG applications?

3. Context window overflow causes what problem?

4. To get consistent, deterministic outputs, you should:

5. What improves retrieval when keyword search alone fails?

Quiz Complete!

0/5

📋 Quick Reference Summary

Key Troubleshooting Areas

  • Prompt Quality: Clear, specific, well-structured prompts with examples
  • RAG Retrieval: Relevant docs, proper top-k, reranking, metadata filters
  • Embeddings: Right model for domain, consistent dimensions
  • Chunking: 512-1024 tokens, appropriate overlap, semantic boundaries
  • Context Window: Stay within limits, prioritize important info
  • Model Settings: Temperature, top_p, max_tokens tuned for task
  • Evaluation: Monitor logs, user feedback, accuracy metrics

Common Fixes

  • Hallucinations: Lower temp, improve retrieval, add citations
  • Slow responses: Optimize DB queries, add caching, batch requests
  • Inconsistency: Temp=0, use seed, standardize prompts
  • Poor retrieval: Hybrid search, reranking, better chunking
Enjoy this tool? Build your own with Super