AI Glossaryinfrastructure

AI Pipeline

A sequence of data processing and AI inference steps that transforms raw input into a useful output, typically involving preprocessing, model inference, and post-processing.

How It Works

An AI pipeline is the end-to-end system that turns a user request into a response. It is rarely just one API call. A production pipeline for a document Q&A system might look like: (1) Accept user query, (2) Preprocess: clean text, detect language, extract keywords, (3) Retrieve: search vector database for relevant document chunks, (4) Rerank: use a cross-encoder to reorder results by relevance, (5) Generate: call the LLM with query + top documents as context, (6) Post-process: validate output format, check for hallucinations, add citations, (7) Return response with sources. Each step can fail independently, so pipelines need error handling at every stage. Common patterns: circuit breakers (stop calling a failing service), fallbacks (use a simpler model if the primary one is down), retries with exponential backoff, and graceful degradation (return a partial answer rather than nothing). Pipeline performance matters. Users expect responses in 1-3 seconds. Optimize by: running independent steps in parallel, caching embeddings and retrieval results, using streaming to show partial results immediately, and choosing the right model size for each step.

Common Use Cases

  • 1Document processing and extraction
  • 2Real-time content moderation
  • 3Search result enrichment
  • 4Automated data analysis
  • 5Multi-stage content generation

Related Terms

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from a knowledge base before generating an answer.

Streaming

A method of receiving AI model output token-by-token in real time as it is generated, rather than waiting for the complete response.

Batch Processing

Processing multiple AI requests together as a group, typically at lower cost and higher throughput than real-time individual requests.

AI Orchestration

The coordination of multiple AI models, tools, and data sources in a unified pipeline to accomplish complex tasks that no single model can handle alone.

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