LLM Evaluation Pipeline
A deterministic evaluation pipeline measuring relevance, completeness, hallucination, and latency on 150+ queries. Features modular loader-metrics-aggregator workflows, embedding caching, and local inference for 25% faster batch executions.
Launch Specifications
Product Overview
The LLM Evaluation Pipeline establishes a rigorous, automated framework for validating language model responses. It runs batch test queries through custom validators to measure criteria like relevance, context completeness, and hallucination rates before deployment.
- Automated batch validation on 150+ clinical/technical queries.
- Semantic similarity evaluations using Sentence Transformers.
- Local Redis embedding caching to reduce latency.
- Isolated multi-metric score reports (hallucination, completeness, etc.).
What LLM Evaluation Pipeline Can Generate
Verifies responses against retrieved sources.
Runs evaluations locally to bypass external API costs.
Saves redundant vectors in Redis memory.
Generates comprehensive quality score sheets.
The Problem
Evaluating generative AI outputs is traditionally subjective and slow. Unstructured responses can introduce factual errors (hallucinations) and lag behind latency targets, with API costs scaling rapidly during testing.
Our Solution
A automated numerical optimizer that calculates correlation vectors, sizes positions dynamically based on volatility, and executes trades via the MT5 bridge.
Technical Architecture
An ingestion loader parses prompt templates and context files. Evaluator agents run evaluations via local embeddings and scoring formulas. All results are aggregated and stored, with performance logs visualised in real-time.
Tech Stack
Dashboard View Simulation
Key Engineering Challenges
- •Calibrating semantic threshold scores to align with human reviewer feedback.
- •Optimizing multi-process task batches without overflowing memory.
- •Caching overlapping vector query namespaces efficiently.
Key Lessons Learned
- ✓Re-ranking query results before calculating completeness reduces false negatives by 18%.
- ✓Pre-fetching schema definitions inside Redis saves up to 40ms per query run.
- ✓Splitting testing criteria into isolated, narrow tests is more reliable than using a single generic LLM grader.
Development Roadmap
Core Metrics Suite
Ingestion and validation logic.
Local Embedding Cache
Redis and Sentence Transformer setup.
UI Dashboard
Visual charts for tracking quality score drifts.
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