3. Data, Science & AI

Comprehensive guide for creating, managing, and maintaining ground truth datasets for AI model evaluation, including annotation, quality control, and versioning.

Guides LLM-based evaluation for automated assessment using prompt patterns, calibration, bias mitigation, and ensemble methods.

A minimal prompt library optimized for low token usage while maintaining high output quality, featuring templates and best practices for efficient AI interactions.

Automates data quality validation across pipelines to ensure accuracy, completeness, and reliability of processed data.

Enables AI agents to autonomously detect, diagnose, and recover from failures without user intervention, improving system resilience.

Monitors data accuracy, completeness, and reliability across data pipelines to ensure high-quality inputs for analytics and AI systems.

Optimizes token usage in LLM applications to reduce costs and improve efficiency through strategic token management.

Centralized platform for managing machine learning models with versioning, metadata tracking, and production deployment capabilities.

Enables LLMs to use external tools for structured data retrieval and integration, enhancing model capabilities with real-world actions.

Tracks and visualizes data origins, transformations, and dependencies across systems to ensure data integrity and traceability.

Manages schema versioning, evolution, and structure changes across databases and event streams to maintain data integrity and compatibility.

Guides detection of Personally Identifiable Information (PII) across systems using regex, NER models, and automated scanning tools for data security.

Provides a structured playbook for backfilling historical data and resolving inconsistencies across multiple data sources.

Comprehensive guide to effective prompt engineering patterns including few-shot, chain-of-thought, and ReAct for enhanced AI model performance.

Provides strategic guidelines for effective AI context retrieval, including selection criteria, exclusion rules, and ordering techniques to optimize RAG system performance.

Detects and manages schema drift in data pipelines to prevent disruptions for downstream data consumers and processing systems.

Provides techniques and tools to interpret machine learning model decisions and communicate them effectively to stakeholders.

Designs chatbot flows, dialog structures, and NLU intents for conversational UX in AI-powered chatbots.

Analyzes, designs, and standardizes new AI skills to ensure consistent and efficient development within AI systems.

Enables querying German Badminton Association (DBV) U19 rankings and player statistics through the badminton-cli tool for specific badminton data requests.

Provides design patterns for building autonomous AI agents capable of reasoning, planning, and task execution.

Provides confidence scoring and calibration metrics to evaluate and interpret machine learning model prediction reliability.

Comprehensive guide for deploying and serving LLM models with optimization, batching, caching, and production infrastructure best practices.

Integrates Databuddy analytics SDK or REST API for tracking features, events, errors, and LLM observability in applications.