3. Data, Science & AI

Provides step-by-step guidance for implementing vector search systems, including indexing and similarity search algorithms for AI applications.

Enables structured output generation and external system integration through LLM function calling (tool use) for AI applications.

Optimizes AI models by reducing size and accelerating inference through quantization, pruning, and advanced model optimization techniques.

Minimal prompt library for efficient LLM usage with low token consumption and high-quality outputs, including templates and best practices.

Enables human intervention in AI decision processes with tracking, auditing, and feedback loops for transparency and accountability.

Delivers semantic search with contextual understanding using language models and vector embeddings for intelligent query resolution.

Performs comprehensive data quality validation across pipelines to ensure accuracy, completeness, and reliability of data assets.

Provides a structured playbook for backfilling historical data and resolving inconsistencies between multiple data sources to ensure data integrity.

Comprehensive guide to using LLMs as judges for automated evaluation with prompt patterns, calibration, and bias reduction.

Provides a step-by-step guide for integrating LLM APIs from OpenAI, Anthropic, Azure, and Cohere into applications.

Comprehensive guide to data augmentation techniques for images, text, audio, and tabular data to enhance machine learning datasets.

Monitors and optimizes data pipeline performance to ensure data meets required timeliness and freshness standards.

Guide for implementing audit trails, logging, and observability in AI agent systems to ensure compliance and debugging capabilities.

Comprehensive guide to vector database patterns, embedding strategies, and similarity search for AI applications using platforms like Pinecone and Qdrant.

Comprehensive guide for monitoring AI/ML systems in production, covering LLMs, RAG applications, and traditional models.

Provides strategies for reducing costs in LLM applications through token optimization and efficient RAG architecture design.

Enables setup of product analytics, metrics tracking, and dashboards to drive data-informed product decisions.

Provides a checklist to remove unnecessary content from prompts, context, and responses, reducing token costs and improving signal-to-noise ratio in LLM interactions.

Step-by-step guide for implementing Retrieval-Augmented Generation (RAG) with LangChain, including data pipeline setup and query optimization.

Manages machine learning experiments, metrics, and model versioning with MLflow and Weights & Biases for reproducibility.

Comprehensive guide for optimizing machine learning models using quantization, pruning, and knowledge distillation techniques.

Expert in data processing, ETL pipelines, transformation, and visualization for actionable business insights.

Expert assistance in optimizing SQL queries and designing efficient database schemas for improved performance and structure.

Enables human review steps in AI workflows to maintain quality, safety, and regulatory compliance.