3. Data, Science & AI

Provides a curated repository for discovering, retrieving, and improving AI prompts, enabling effective prompt templates for LLM interactions.

Enables creation, editing, and analysis of spreadsheets including formulas, formatting, and data visualization for data processing tasks.

Adds product analytics events to track user interactions within Metabase frontend for data-driven insights and behavior analysis.

Calculates key financial ratios and metrics from financial statements to support investment analysis and decision-making.

Guidelines for creating and updating modes in PostHog AI agent to control tool usage, prompts, and prompt injections for optimized performance.

Optimizes vector index performance for latency, recall, and memory by tuning HNSW parameters and quantization strategies in AI search systems.

Enables analytics engineering with dbt for organizing data models, testing, documentation, and incremental data transformations.

Builds Retrieval-Augmented Generation (RAG) systems for LLMs using vector databases and semantic search to enable knowledge-grounded AI applications.

Designs PostgreSQL schemas with best practices, including data types, indexing, constraints, and performance patterns.

Designs LLM applications with agent architectures, memory management, and tool integration using LangChain 1.x and LangGraph.

Automates end-to-end machine learning pipeline creation, covering data preparation, model training, validation, and production deployment.

Combines vector and keyword search to improve retrieval accuracy in RAG systems and search engines.

Optimizes Apache Spark job performance through partitioning, caching, shuffle tuning, and memory configuration for efficient data processing pipelines.

Enables implementation of data quality validation using Great Expectations, dbt tests, and data contracts for robust data pipelines.

Optimizes LLM performance, reliability, and output quality using production-ready prompt engineering patterns.

Optimizes SQL queries and database schemas using indexing and EXPLAIN analysis to enhance performance and resolve slow queries.

Transforms data into compelling narratives using visualization and context for stakeholder presentations and executive reports.

Enables comprehensive evaluation of LLM applications through automated metrics, human feedback, and benchmarking to measure AI performance and quality.

Guides optimal KPI dashboard design with metric selection, visualization best practices, and real-time monitoring patterns.

Optimizes embedding models and chunking strategies for semantic search and RAG applications in AI systems.

Enables efficient semantic search and nearest neighbor queries through vector database optimization for AI-driven retrieval systems.

Calculates portfolio risk metrics including VaR, CVaR, Sharpe ratio, Sortino ratio, and drawdown for risk assessment.

Analyzes meeting transcripts and recordings using AI to identify communication patterns and provide actionable feedback for leadership development.

Debugs LangChain and LangGraph agents by fetching and analyzing execution traces from LangSmith Studio for error investigation and performance insights.