Use case

LLM pipelines and serverless AI workflows

Stage retrieval, moderation, tool calls, and summarization as separate functions so retries, traces, and cost control apply per step—not to one oversized prompt.

Model a pipeline →AI agent backend

Workload and what breaks

Why single-prompt LLM workflows are hard to retry

One mega-prompt cannot retry retrieval without re-running moderation or tool calls—LLM pipelines need boundaries.

Costs balloon when every branch re-embeds the same context instead of caching structured retrieval output.

Where shortcuts fail

Why notebook scripts do not replace LLM pipelines

Exploratory notebooks lack durable graphs, retries, and cost control for production AI workflows.

How Inquir helps

Stage LLM work as deployable functions

Each stage is deployable and loggable; compose with pipelines for async gaps between model calls.

Tool calls stay HTTP functions with explicit auth—consistent with serverless AI agents elsewhere.

What you get

LLM pipeline stages to split for observability

Retrieve

Isolate embedding and search calls.

Moderate

Fail fast before expensive generation.

Call tools

Call tools with tight input validation.

Summarize

Compress for storage or user display.

What to do next

How to stage LLM work with Inquir pipelines

Draw dataflow

Name inputs/outputs per box.

Codify

Implement each box as a function or pipeline step.

Measure cost

Track tokens and wall time per stage.

Code example

Example workload stages

Concrete production flow: document intake -> classify -> retrieve -> summarize -> save/notify.

flow.js

// document intake -> classify -> retrieve -> summarize -> save/notify

When it fits

Use pipelines when…

When this works

Multi-model flows
Human-in-the-loop handoffs
Long-running enrichment

When to skip it

Single prompt demos

FAQ

Why split an LLM workflow into stages?

Retries, cost attribution, and debugging improve when retrieval, moderation, tool calls, and summarization are separate steps with their own logs.

Streaming tokens to end users?

Keep user-visible streaming at the boundary; internal stages can use request/response for simpler failure handling and replays.

How do I control cost across stages?

Measure tokens and wall time per stage in observability; cap expensive steps with budgets and short-circuit when moderation fails.