Troubleshooting
Encountering issues with a bot? This guide is here to help and provides solutions to common problems, ensuring you can quickly resolve any issues and maintain smooth operation.
Problem-Solving Chain
When encountering issues with the Blockbrain Knowledgebots, please follow this escalation path for efficient problem resolution:
1. Self-Help Resources
First, try to resolve the issue independently using these resources:
Blockbrain User Guide
Check error messages and common problems
Review step-by-step solutions
Follow recommended fixes
Blockbrain User Guide Bot
Ask direct questions
Get immediate automated responses
Access specific problem-solving guidance
You can find the Blockbrain User Guide Bot as a Default Bot on your Dashboard.
2. Internal Support
If self-help resources don't resolve your issue, escalate internally:
Contact Your Company Builder
Share detailed problem description
Provide relevant screenshots
Explain steps already taken
Consult Your Company Admin
Escalate if Builder cannot resolve
Provide previous communication history
Detail all attempted solutions
3. Blockbrain Expert Support
The Blockbrain team is always here to help with complex issues that couldn't be resolved through other channels. Feel free to reach out when:
You've explored self-help resources and internal support options
Your Company Admin recommends escalation
You need specialized expertise for your specific case
Our dedicated team will be happy to assist you with:
In-depth technical analysis
Custom solutions
Expert guidance
This structured approach helps us provide you with the fastest and most effective support possible. While our team is always ready to help, many issues can be resolved quickly through self-help or internal support channels, saving you valuable time.
Why is the bot not giving me the desired answers?
Sometimes, your AI assistant might not provide the answers you're looking for. While this can be frustrating, it's often easily remedied. In this section, we'll explore the most common reasons why a bot might not give desired answers and offer practical solutions.
Whether it's issues with the knowledge base, query formulation, or bot settings - we'll help you identify the cause and optimize your AI assistant. Let's work together to ensure your bot reaches its full potential and delivers the precise, relevant answers you need.
Common Chat Room Troubles and Solutions
1. Data Room Selection
Problem:
Receiving irrelevant or incomplete answers
Not getting context-specific information
Solution:
Select the appropriate data room for your query
Why Data Rooms Matter:
Each data room contains specific knowledge bases
Enables more accurate and relevant responses
Helps organize information by topics or departments
Ensures data privacy and access control
Improves response quality through focused context
2. Web Search Feature
Problem:
Web search running for every query when not needed
Slower response times due to web searching
Too much external information in responses
Solution:
Toggle Web Search feature on/off at the bottom of the chat field
Enable only when you need current information
Disable for queries that only require internal knowledge
About Web Search:
Enhances responses with current web information
Useful for up-to-date topics and external references
Can be selectively used based on query needs
Prompting: A (very) Quick Guide
Length Matters
Too long: Prompts that are too long can confuse the AI or exceed token limits, potentially leading to incomplete or inaccurate responses.
Too short: Prompts that are too short may lack the necessary context, making it difficult for the AI to understand your request fully.
Aim for concise yet informative prompts: To get the best results, aim for prompts that are concise yet informative, striking a balance between providing enough context and keeping the query focused and manageable.
Keep It Simple
Avoid using overly complex or nested prompts, as these can confuse the AI and lead to unclear or inaccurate responses.
Break down complex queries into smaller, more manageable parts. This approach allows the AI to process each component of your request more effectively, resulting in clearer and more accurate answers.
Provide Sufficient Context
When crafting your prompt, be sure to include relevant background information that provides context for your query. This helps the AI understand the full scope of your request.
Additionally, specify the desired format or style of response you're looking for, or indicate where the AI should retrieve the desired data from. Being clear about these details helps ensure that the AI's response meets your expectations and needs.
Avoid Contradictions
Ensure that your instructions are consistent throughout your prompt to avoid confusing the AI.
Before submitting your query, double-check for any conflicting requirements or contradictory information.
Be Specific
When formulating your prompt, use clear and unambiguous language to communicate your request effectively. Avoid vague terms or phrases that could be interpreted in multiple ways.
State your expectations explicitly, clearly outlining what you want the AI to do or provide. Being direct and specific in your instructions helps ensure that the AI understands your request accurately and can deliver a response that meets your needs.
Use Appropriate Formatting
When providing multiple instructions in your prompt, utilize bullet points or numbering to clearly separate and organize each step or request. This structured approach makes it easier for the AI to process and address each part of your query.
Highlight key points or crucial information using formatting techniques such as bold or italics. This emphasis helps draw attention to the most important elements of your prompt, ensuring that the AI focuses on the essential aspects of your request.
Iterate and Refine
If your first attempt at prompting doesn't yield the desired results, don't be discouraged; try rephrasing your query using different words or structures. Effective prompting often requires some experimentation.
Pay attention to the prompts that generate successful responses and learn from them. Apply the techniques and patterns you've found effective in these successful prompts to future queries, as this can help improve your overall prompting skills and results.
Remember, effective prompting is often an iterative process. Don't hesitate to experiment and refine your approach based on the results you receive.
Connected Data Functionality
1. Difference between Files and VectorDB
Connected Files:
Context/Token Limit: Connected files count directly towards the context/token limit of the conversation. Large files can quickly exceed this limit, leading to incomplete or truncated responses.
Usage: Ideal for smaller datasets or when immediate context is crucial.
Issue: Large files can quickly consume the available token limit for a conversation. This can lead to incomplete or truncated responses from the AI, as it cannot process all the necessary information within the token limit.
VectorDB:
Efficient Storage: Stores data in a vector format, allowing for efficient retrieval and processing.
Token Management: Helps manage token limits more effectively by indexing data, making it suitable for larger datasets.
Usage: Best for extensive datasets where fast search and retrieval are necessary.
Issue: Queries that require nuanced understanding or context might be challenging for VectorDB to handle effectively. This can result in less accurate or relevant responses if the query context is not well-defined.
2. Contextual Overlap
Problem: When a query generates hits in different databases or topics, it can lead to ambiguous or incoherent answers.
Example:
HR Database: "Christmas" might relate to holiday regulations.
Marketing Database: "Christmas" might relate to campaigns and advertising materials.
Solution:
Refine Queries: Clearly specify the desired context in your queries to avoid ambiguity.
Database Segmentation: Consider segmenting your data into more narrowly focused databases to minimize overlap.
3. Database Size and Topic Variety
Issue: Overly large databases or databases with a wide variety of topics can exacerbate contextual overlap, leading to less precise answers.
Recommendation:
Thematic Structuring: Organize your data into smaller, thematically focused databases. This reduces the likelihood of overlap and improves response accuracy.
Regular Audits: Periodically review and reorganize your databases to ensure they remain focused and relevant.
4. Reviewing Advanced Settings
Problem: The Knowledge Bot does not understand my uploaded Image or Table. Ensure the following advanced settings are correctly configured to optimize data processing and retrieval:
Smart Image Processing:
Function: Enhances the AI's ability to interpret and utilize images within your data.
Configuration: Verify that this setting is enabled if your data includes significant visual content.
Smart Table Processing:
Function: Improves the AI's handling of tabular data, ensuring accurate interpretation and response generation.
Configuration: Enable this setting for databases with extensive tabular information.
Chunk Size and Chunk Overlap:
Chunk Size: Adjust the size of data chunks to balance between context and token usage.
Chunk Overlap: Modify overlap settings to ensure continuity and coherence in responses, especially for large documents.
Additional Tips
Iterative Refinement:
Experimentation: If the initial setup doesn’t yield the desired results, iteratively refine your settings and queries.
Learning from Success: Analyze successful prompts and configurations to apply those techniques to other queries.
User Feedback:
Engagement: Encourage users to provide feedback on the AI's responses to identify areas for improvement.
Continuous Improvement: Use this feedback to continuously refine and enhance your data and settings.
By considering these points, you can significantly optimize the performance of your Connected Data functions and obtain more precise, relevant answers from your AI assistant.
Limitations of the Knowledge Bot
1. Temporal Awareness
Limitation:
The bot cannot determine when a document was uploaded to the database.
It cannot answer queries about the most recently uploaded document.
Implication:
The bot can only provide information based on the content of the documents, not their upload dates.
2. Web Search Dependency
Limitation:
Without Web Search enabled, the bot lacks information on current topics.
It only knows up to the point when it was last trained.
Implication:
For up-to-date information, ensure Web Search is turned on.
The bot's knowledge is static and does not include recent developments unless Web Search is used.
3. Knowledge Scope
Limitation:
The bot can only provide information it has been trained on or that is available in the connected databases.
It cannot generate or infer information outside its training data or connected sources.
Implication:
Ensure the correct database is linked and the context is provided for accurate responses.
The bot cannot provide company-specific information unless it is included in the connected data.
4. Token Limit
Limitation:
There is a token limit for files uploaded and connected to the bot.
Extremely large files cannot be processed.
Implication:
Break down large documents into smaller parts if necessary.
Ensure files are within the token limit for successful processing.
5. Text-Based Output
Limitation:
The bot's output is limited to text responses.
While it can understand and process files like Excel or PDF, it cannot generate or output these formats.
Implication:
Use the bot for text-based queries and responses.
For file-specific outputs, manual handling outside the bot is required.
Understanding these limitations helps in setting realistic expectations and utilizing the Knowledge Bot effectively. Always ensure the correct settings and context for optimal performance.
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