TruLens

Integrate TruLens with the RAG pipeline using its Python SDK to trace context retrieval, LLM interaction, and response generation.

Apply built-in metrics like 'Context Relevance' and 'Groundedness' to objectively evaluate RAG performance.

Run multiple experimental iterations of the RAG app with varying prompts, vector stores, or LLMs.

Compare evaluation results on a TruLens leaderboard to identify the best-performing version based on key metrics.

Utilize trace-level insights provided by TruLens to debug specific issues and refine the RAG architecture.

Deploy TruLens to continuously trace agent executions, leveraging its OpenTelemetry integration for seamless data capture.

Monitor key evaluation metrics to detect anomalies in agent performance, output quality, or critical component failures.

When an issue is flagged, drill down into specific traces to inspect tool calls, intermediate thoughts, and LLM outputs at each step of the agent's execution.

Use TruLens' trace visualizations to understand the agent's decision-making process and pinpoint the root cause of errors or suboptimal performance.

Iterate on agent prompts, configurations, or underlying logic based on concrete insights derived from observed traces and evaluations.

Instrument different summarization models with the TruLens SDK to capture their inputs and generated summary outputs.

Define custom metrics or utilize built-in ones for coherence, comprehensiveness, factuality, and checks for harmful language or bias.

Run a batch evaluation across a diverse dataset using all summarization models and capture their traces.

Analyze the TruLens metrics leaderboard to compare model performance against the defined criteria and identify trade-offs.

Select the best-performing and safest model for deployment, with confidence in its quality characteristics supported by objective metrics.