Composable Prompts

An Application Platform for Large Language Models — a platform crafted to bridge the gap between LLMs and enterprise applications that includes tools and frameworks that helps developers and architects to craft precise, efficient interactions with LLMs. Users can design prompt templates, manage data schemas, and visually orchestrate complex interaction workflows.

Interaction Design and Management Features:

• Prompt Designer - Used to visually create prompt templates, input-output data schemas, and cache policies to fine-tune LLM interactions.
• Templates - Visual designer templates, alongside data schemas that integrate with enterprise systems.
• Versioning & Approval - Versions, tags, and approval processes can be managed to ensure a structured interaction development workflow.

The Prompt Library

• Prompt Assistance - AI-driven recommendations for prompt designs, backed by custom training to refine LLM responses.
• PromptSegment Library - A library of prompt segments ranging from data privacy to domain-specific nuances.
• Optimized LLM Collaboration - Different LLM engines can be made to work in tandem, leveraging each engine's strengths based on task requirements.

API Service

• API Endpoints - Exposes interaction definitions as API endpoints, allowing integration and adaptability across systems.
• Schema Validation - Maintains the integrity of data with rigorous schema validation, ensuring data consistency across LLM interactions.
• Performance Tuning - Optimizes the API's performance to reduce latency and improve the responsiveness of LLM calls.

Cache Optimization

• Cache Service -Caching mechanisms to minimize redundant LLM calls and improve overall system efficiency.
  
• Cache Refresh Control - Determines when and how cache data is refreshed to maintain up-to-date interactions with the LLMs.
• Vector Indexing - Includes techniques for quicker data retrieval and accelerated cache access.

• Test - Used to validate interactions, ensuring ongoing consistency and adhering to enterprise standards.
• Debugging - Dive deep into LLM results, track iterative changes, and decipher variations to fine-tune LLM interactions.
• Monitoring - LLM performance metrics to monitor quality, latency, and overall system health for proactive management.