Chronicle

AI-powered Multi-LLM platform for prompt versioning, A/B testing, and execution tracking.

RoleDeveloper & Architect

Timeline2025 – 2026

Reading Time1 min read

Links

PythonFastAPIPostgreSQLReactDockerRedisLangChainGroq API

Context & Problem

Enterprise LLM integration often suffers from regression, silent errors, and opaque pricing when prompts are managed as informal, ad-hoc text fragments. Chronicle was engineered as a centralized, production-grade LLMOps control plane to bring software engineering discipline to the prompt lifecycle. The system mathematically enforces Git-inspired prompt immutability, provides real-time transaction telemetry, and supports multi-objective optimization to balance generative accuracy against API execution costs.

Architecture

System pipeline layout representing coordination blocks and execution pathways designed for this solution.

Implementation

Architected a decoupled three-tier system comprising a React-based frontend dashboard, an asynchronous FastAPI routing layer, and a PostgreSQL database featuring native JSONB fields for dynamic model settings.
Enforced a database-first transaction contract: every LLM execution request is pre-recorded in the database with a 'pending' state before invoking remote APIs, preventing audit trail data loss during network crashes.
Integrated a multi-model execution pipeline using LangChain to benchmark prompt performance across diverse foundation models (GPT-4, Claude, Llama 3, Gemini) simultaneously.
Designed an evaluation suite supporting exact-match parsing, LLM-as-a-Judge grading rubrics, and Platt-scaling inspired Mean Calibration Error (MCE) confidence calculations.
Implemented a Pareto Frontier analyzer utilizing the geometric knee-point heuristic to identify and recommend the single most cost-effective prompt-model configuration on a scatter plot.

Challenges & Trade-offs

CHALLENGE: API bottlenecks and high latency during multi-judge LLM-as-a-Judge evaluations.

RESOLUTION: Migrated evaluation pipelines to Groq's high-throughput API, integrated Redis query caching, and offloaded evaluations to ARQ background workers, dropping latency to sub-2s.

CHALLENGE: Storing dynamic, vendor-specific LLM parameters without violating relational database schemas.

RESOLUTION: Implemented native JSONB columns in PostgreSQL to combine strict transactional audit trail integrity with highly queryable, schema-less document storage.

CHALLENGE: Risk of prompt drift and version mismatch across collaborative engineering environments.

RESOLUTION: Enforced application-layer prompt immutability where any modification generates a new version row with a unique SHA-256 hash, using an append-only promotion audit ledger (Alias_History).