Advanced AI-Evals & Monitoring

- Advanced AI-Evals & Monitoring - Scale LLM-judge for bulk multimodal outputs - Build dashboards comparing judge accuracy vs IR metrics - Implement auto-gate builds if accuracy drops below 95% - Agent Failure Analysis Deep Dive - Create transition-state heatmaps & tool states visualization - Construct failure-matrices with LLM classification - Develop systematic debugging workflows - Enhancing RAG with Contextual Retrieval Recipes - Use Instructor-driven synthetic data (Anthropic GitHub) - Integrate web-search solutions (e.g., exa.ai) - Apply LogFire, Braintrust augmentations - Implement Cohere reranker + advanced logging - Advanced Synthetic & Statistical Validation - Generate persona-varied synthetic questions (angry/confused personas) and rewrite questions for better retrieval - Perform embedding-diversity checks and JSONL corpus structuring - Work with multi-vector databases - Build parallel experimentation harness using ThreadPoolExecutor - Strategic Feedback Collection - Collect feedback with different types; use binary feedback (thumbs up/down) instead of stars - Distinguish between two segment types: lack of data vs lack of capabilities - Address common but fixable capability issues - Dynamic Prompting & Validation - Build dynamic UI with chain-of-thought wrapping using XML or streaming - Incorporate validators with regex (e.g., checking fake emails generated by LLM) - Data Segmentation & Prioritization - Segment data based on patterns - Apply Expected Value formula: Impact × Percentage of Queries × Probability of Success - Topic Discovery with BERTopic - Configure and apply BERTopic for unsupervised topic discovery - Set up embedding model, UMAP, and HDBSCAN for effective clustering - Visualize topic similarities and relationships - Analyze satisfaction scores by topic to identify pain points - Create matrices showing relationship between topics and satisfaction - Identify the "danger zone" of high-volume, low-satisfaction query areas - Persona-Driven Synthetic Queries - Generate diverse queries (angry, curious, confused users) to stress-test retrieval and summarization pipelines - Regex & Schema Validators for LLM Outputs - Add lightweight automated checks for emails, JSON formats, and other structural expectations - Segmentation-Driven Summarization - Build summarization-specific chunks, integrate financial metadata, and compare with BM25 retrieval - Failure-Type Segmentation - Classify failures into retrieval vs generation errors to guide improvement priorities - Clustering Queries with BERTopic - Use UMAP + HDBSCAN to group user queries into semantically meaningful clusters - Mapping Feedback to Topics - Overlay evaluator scores onto clusters to identify weak performance areas - Danger Zone Heatmaps - Visualize query volume vs success rates to prioritize high-impact fixes - Feedback-to-Reranker Loop - Build iterative reranking systems driven by topic segmentation and evaluation feedback - Dynamic Prompting for Tool Selection - Teach LLMs to output structured tool calls reliably (JSON schema, guardrails, few-shots) - Tool Disambiguation and Clarification Loops - Design prompts that force models to ask clarifying questions before executing - XML-Based CoT Streaming for Agents - Output reasoning traces in structured XML-like format for real-time dashboards or UIs - Production-Grade Project - Deploy a full RAG + fine-tuned LLM service - Add multiple tools with RAG and implement tool routing - Include multimodal retrieval, function-calling, LLM-judge pipeline, and monitoring - Achieve ≥ 95% end-to-end task accuracy - Exercises: AI Evaluation & Monitoring Pipeline - Build LLM-as-judge evaluation pipelines with accuracy dashboarding - Apply BERTopic for failure analysis and danger zone heatmaps - Generate persona-driven synthetic queries for stress-testing - Implement automated quality gates with statistical validation