3. Data, Science & AI

Enhances semantic retrieval quality using Hypothetical Document Embeddings to align queries with document content, improving RAG performance in ambiguous cases.

Manages database schema versioning, change tracking, and audit trails across environments for consistent database evolution.

Comprehensive suite for designing, optimizing, and managing AI prompts using Chain-of-Thought, few-shot learning, and versioning to enhance model accuracy and lifecycle management.

Designs efficient database schemas with normalization, indexing, and migration patterns for SQL and NoSQL databases.

Enables real-time token streaming for LLM responses using Server-Sent Events and tool call integration.

Decomposes complex queries into atomic components to improve multi-concept retrieval coverage and accuracy in AI-driven search systems.

Uses vision-language models for image captioning, document understanding, and visual QA tasks.

Optimizes LLM inference performance via vLLM, quantization, and speculative decoding for low latency and high throughput.

Enables persistent semantic memory across user sessions for LLM applications, allowing recall of past interactions and context.

Optimizes LLM application costs through Redis-based semantic caching, reducing redundant API calls with vector similarity matching.

Generates text embeddings for semantic search, similarity matching, and document analysis using vector representations.

Provides testing patterns for LLM-based applications, including mocking responses and validating structured outputs.

Enables structured LLM tool interactions through function schemas, execution loops, and external API integration for AI agents.

Automates quality assessment of LLM outputs using LLM-as-judge patterns and quality gates for AI application reliability.

Enables multimodal RAG with CLIP, SigLIP 2, and Voyage Multimodal-3 for image-text retrieval and visual search.

Automatically loads session context from mem0 and knowledge graph at startup for AI agents and RAG systems.

Compresses conversation history to maintain LLM response quality within token limits using anchored summarization and validation.

Enables local inference of large language models using Ollama, supporting development, CI pipelines, and cost efficiency with model selection and LangChain integration.

Improves RAG systems using hybrid BM25-vector search to maintain context and reduce retrieval failures in large language model applications.

Curates high-quality datasets for AI model evaluation, defining quality criteria and multi-agent analysis workflows for test data.

Enables building LLM agent workflows with parallel execution, persistence, and human-in-the-loop capabilities using LangGraph's functional API pattern.

Provides production-ready hybrid search using PGVector HNSW and BM25 with Reciprocal Rank Fusion for semantic and metadata-based retrieval.

Retrieves past decisions and patterns from knowledge graphs for informed decision-making and pattern recognition.

Provides API integration patterns for real-time voice processing, speech-to-text, and text-to-speech using leading AI models and voice agents.