LLM Transparency
Traditional LLMs and providers often operate as opaque black boxes, making it challenging to understand the reasoning behind their decisions. Botpress’s BSF addresses this challenge by exposing the decision-making pathways of LLMs, allowing users to see why certain outputs were generated and how they align with brand standards. The core principle is simple: when you can understand and influence an LLM’s decisions, you can protect your brand.Key Features
Policy Agent

Usage
- Define constraints, including YAML or JSON configurations.
- Set gates to filter out undesirable responses based on specific keywords, patterns, or context cues.
- Identify policies or behaviors your agent should adhere to during conversations.
RAG Safety

Usage
- Access a visual interface that displays selected chunks with contextual relevance scores.
- Modify data chunk selections manually or set up automated rules to refine data retrieval criteria.
- Log and audit the data flow to track the decisions made during the query process.
LLM Inspector

Usage
- Inspect decision trees that outline response logic and action selection.
- View confidence scores, logic paths, and influence factors contributing to specific outputs.
- Adjust weighting and influence factors to steer future decision-making processes
HITL
