Build and Apply Advanced Features
This section explores advanced Knowledge Bot features, allowing you to customize settings for better accuracy, optimized search results, and improved responses.
Knowledge Bot Settings
In the upper left corner of the Knowledge Bot, you'll find the gear icon, which opens the bot settings. Here, you can customize and enable various features to tailor the bot to your needs.
Capabilities & Skills: Adjust search methods and activate advanced features
LLM Models: Choose the language model that powers your bot's responses, balancing performance, accuracy, and cost
Database: Select the default database for all data rooms
Agents: Set up or activate customizable prompts for more efficient use
Automations: Set up workflows for more efficient prompting
Action Settings: Adds quick-access buttons to the Knowledge Bot navigation and enables advanced features like Intent Agent and System Agent for enhanced search and automation
Note: If you don't see any bot settings, you may not have access to them. Inquire to the admin of the bot regarding its access.
Large Language Models (LLMs)
Blockbrain allows users to select from a variety of Large Language Models (LLMs) based on their specific needs. Each model has unique strengths in terms of response quality, speed, cost efficiency, and specialized capabilities. Some other factors to consider are: Context window size, Performance and Hosting location.
Model Modifiers
Model modifiers allow you to fine-tune your Knowledge Bot’s behavior by adjusting key parameters that influence how it processes and generates responses. These settings help balance creativity, precision, and relevance based on the nature of your task.
Why Use Model Modifiers?
By adjusting model modifiers, you can:
Ensure responses align with specific business objectives
Optimize results for creative, technical, or research-based tasks
Improve efficiency and consistency in AI-generated outputs
Knowledgebase Management
Embedding Models
When creating a database in Blockbrain, you are prompted to choose an embedding model. These models convert text (such as documents, files, or data) into numerical representations called embeddings. This allows the system to search, compare, and retrieve relevant content based on meaning, not just keywords.
Agents
Agents are reusable, customizable prompt shortcuts that help streamline tasks, saving time and improving productivity. They allow you to automate specific queries, ensuring consistency and efficiency in responses.
Why use Agents?
Efficiency: Reduce repetitive typing and automate frequently used prompts.
Consistency: Ensure responses follow a structured and standardized format especially across scaling teams.
Customization: Tailor Agents to fit specific workflows and your needs.
Collaboration: Share Agents within your organization for uniform responses.
Faster Decision-Making: Instantly generate reports, summaries, and recommendations.
It is possible to create custom Agents or take advantage of Blockbrain’s pre-made Agents for quick and easy implementation. Simply activate the pre-made agents or create your organization's own custom agent through the bot settings.
Workflows
Workflows allow you to automate multi-step prompts, making complex interactions more structured and efficient. Unlike Agents, which function as single-prompt shortcuts, Workflows guide the AI through a sequence of prompts to ensure a more accurate and refined final response.When asking for too much in a single prompt, the AI may struggle to process and provide precise answers. Workflows break down complex queries into manageable steps, improving accuracy and relevance at each stage.
Why use Workflows?
Improved Accuracy: The AI delivers more precise responses when given structured, step-by-step prompts.
Better Context Retention: Each step builds on previous answers, leading to a more cohesive final result.
Scalability: Automate repetitive, multi-step tasks to save time and improve efficiency.
Intent Agent
The Intent Agent is a feature that users can enable to enhance AI efficiency. By providing descriptions for folders, the Intent Agent enables the AI to sift through the database and assess which information is most relevant to users. The AI uses these folder descriptions to identify and prioritize the folders most likely to contain relevant information.
This feature transforms databases from simple file repositories into structured resources optimized for quick and accurate data retrieval.
Why use Intent Agent?
Improved Efficiency: Reduces time spent scanning irrelevant folders by narrowing the search to relevant areas.
Optimized Workflows: Simplifies the process of locating the correct folder, especially as databases grow larger and more complex.
Faster Results: Quickly identifies and retrieves the most relevant information, saving time.
Resource Optimization: Conserves computational resources by prioritizing the processing of relevant data.
Insights
Insights are are text-based notes that you can input yourself, or allows you to store and retrieve previously saved AI interactions, making it easy to reuse knowledge from past conversations. This is useful for ensuring consistency in responses and retaining key learnings across teams.
These saved insights act as a personal knowledge base, helping users retain important AI-generated information, streamline workflows, and maintain consistency across multiple interactions. Unlike databases, Insights are stored in full context without chunking, preserving the original message structure for improved retrieval and reuse.
When to Use Insights
When past AI-generated responses need to be referenced frequently
When team members share refined prompts or key findings from Data Rooms
When specific contextual knowledge should be stored for quick access
Contribute Knowledge
Contribute Knowledge allows you to save an Insight—an AI-generated message or conversation—directly into your database. Instead of storing it in the Insights section, the content is saved within a selected database, making it accessible to team members who have access to that database.
This method is ideal for collaborative work, ensuring that key insights are systematically stored and structured within a shared knowledge base. Additionally, team members who are subscribed to the database will receive email notifications about new contributions, keeping everyone updated without manual coordination. However, since databases undergo chunking, the information may be divided into smaller sections, which could slightly affect retrieval accuracy depending on the query.
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