Tagged rag

Score RAG answer quality with RAGAS on a corpus and training questions

Load-test a deployed RAG Blueprint server with profiling and aiperf from a YAML config

Scaffold a production FastAPI backend with domain modules, RAG and agents

Score RAG retrieval and citation quality from JSONL fixtures in offline evals

Write interview questions for AI agent developer roles, covering RAG and MCP

Ready-made AWS prompts for scaffolding MVPs, RAG chatbots and security reviews

Search the web and a local document store for finance information with RAG

Run semantic search over ingested documents with LlamaIndex or ChromaDB RAG

Test LLM apps for prompt injection, indirect RAG attacks and MCP tool abuse

Convert Office files and PDFs to clean Markdown for summaries and RAG ingestion

Set up pgvector semantic search for embeddings, RAG and nearest-neighbor queries

Build production LLM apps with RAG, vector search, multimodal AI and agents

Test LLM and RAG systems for system prompt leaks, data extraction and poisoning

Measure RAG retrieval and answer quality with synthetic QA and faithfulness checks

Build RAG pipelines with document loaders, text splitting, embeddings and vector stores

Orchestrate ML training, batch inference and RAG indexing with Prefect or Airflow

Build RAG infrastructure with embeddings, chunking, hybrid search and reranking

Measure RAG retrieval quality, groundedness and hallucinations in regression tests

Run vector databases like Qdrant, Weaviate, pgvector and Pinecone for RAG search

Write declarative LM programs in DSPy and auto-optimize prompts and RAG pipelines

Call AWS Bedrock foundation models, create embeddings and build RAG pipelines

Build LLM-powered features with prompt engineering, RAG and chatbot patterns