数据与 AI技能

Milvus distributed vector database configuration for large-scale RAG applications

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Token-efficient prompt compression techniques for cost optimization

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Orchestrate workflows across multiple applications and APIs — inter-app coordination, data handoff, and multi-system task completion.

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Cross-encoder reranking and MMR diversity filtering for improved retrieval quality

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Prompt injection detection and prevention for secure LLM applications

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Select optimal data structure based on operation requirements

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OpenTelemetry instrumentation for LLM applications with distributed tracing

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LLM-based zero-shot and few-shot classification for flexible intent detection

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LlamaIndex agent and query engine setup for RAG-powered agents

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SetFit few-shot learning for efficient intent classification with minimal data

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Provide implementations of advanced data structures

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Query expansion, HyDE, and multi-query generation for improved retrieval

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Structured prompt template creation with variables, formatting, and version control

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Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.

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Batch embedding generation with caching, rate limiting, and multiple provider support

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Pinecone vector database setup, configuration, and operations for RAG applications

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Chain-of-thought and step-by-step reasoning prompts for complex problem solving

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Few-shot example generation and optimization for improved LLM performance

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Constitutional AI and safety guardrail prompts for aligned LLM behavior

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LangChain retriever implementation with various retrieval strategies for RAG applications

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Guardrails AI validation framework setup for LLM applications. Implement input/output validation, safety checks, and structured output enforcement.

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WASM-based instant code transforms for simple tasks, achieving 352x speedup over LLM inference with zero cost.

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System and API design guidance covering component boundaries, data flow, integration patterns, and scalability considerations.

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Recover missing spreadsheet values from row and column totals, percentage shares, year-over-year changes, CAGR relationships, and cross-sheet constraints.

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