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Troubleshooting

Types of problems​

When building your assistant, you may encounter that it does not provide the correct answers and that may be due to different types of problems. This section will help you identify and solve them.

Information Problems​

Problem Description The system fails to provide adequate responses due to missing information, document inconsistencies, or poor formatting that prevents proper model interpretation.

Solutions

  • Add, remove, or modify documents with functional team support
  • Reload documents with improved formatting
  • Use custom separators and preview blocks before uploading
  • Perform manual document modifications when needed

Chunking Problems​

Problem Description Issues arise during document segmentation affecting how information is processed and retrieved, particularly with chunk size management and overlap settings.

Solutions

  • Adjust chunk size parameters
  • Modify overlap settings
  • Implement chunk separators
  • Configure manual blocking
  • Improve preprocessing techniques

Information Retrieval Problems​

Problem Description Challenges with temporal relevance, information freshness, and semantic search complexity, especially when dealing with similar extracts or subtle content differences.

Solutions

  • Implement date-based reordering
  • Use fixed extracts
  • Apply Cross-Encoder reranking
  • Conduct multiple targeted searches by topic

Context Problems​

Problem Description The assistant lacks sufficient background information to resolve queries successfully, leading to incomplete or incorrect responses.

Solutions

  • Modify system prompt for global context needs
  • Use lessons for general context
  • Use document metadata for document-specific context
  • Implement block metadata for extract-specific information

Flow and Interaction Style Problems​

Problem Description Issues with communication patterns and interaction style that affect the user experience and conversation quality.

Solutions

  • Modify system prompts with specific instructions
  • Implement FAQ structures
  • Consider fine-tuning options
  • Add example-based instructions (few-shot)

Generation Problems​

Problem Description The language model struggles with complex reasoning or calculations despite having all necessary information and context.

Solutions

  • Switch between LLM providers (OpenAI, Google, Anthropic, Mistral, etc.)
  • Upgrade to more powerful models (e.g., GPT-4o instead of GPT-4o-mini)
  • Use alternative prompting techniques
  • Implement specialized tools for complex tasks

Platform's troubleshooting and monitoring module​

Our platform has a built-in troubleshooting and monitoring module that will help you identify and solve problems with your assistant. Think of it as a debugger tool that also provides detailed logs of what the assistant is doing and why it is doing it.

If you head to Activity menu > Troubleshoot, you will be able to see all the conversations to select the one that you want to debug. You can find the desired one quickly by using the filters.

Troubleshooting

Once you select the conversation, you will be able to see the full conversation history, containing:

  • The user's message and its attachements if any
  • The assistant's request to the tools and searches with its parameters
  • The information returned by the tools
  • The assistant's answer provided to the user,
  • The feedback provided by the user, if any
  • The prompt sent to the LLM

The following image shows an example of a conversation:

Troubleshooting