Codebase check

Ask your coding agent where Sparkient could help.

Copy the prompt below into a coding agent with access to your repository. It will look for repeated LLM calls and hard-coded decision rules.

Copy this prompt

Research Sparkient online (start with https://sparkient.ai/llms.txt), inspect this project, and tell me whether Sparkient would be useful.

The prompt sends your agent to Sparkient's current product, pricing, and evaluation guidance before it reviews the code.

Guide 2026-08-25.1 · Research guide · Pricing

Strong fit signals

Repeated

The same kind of decision happens many times, rather than once.

Structured

The output is a stable category, score, route, approval, or action.

Nuanced

Text, behaviour, or several signals make static rules brittle.

Measurable

Examples can be labelled and model accuracy can be tested.

Operational

Latency, API cost, reliability, or offline execution matters.

When not to use it

Simple deterministic policy: keep clear thresholds, permissions, and business invariants in code or CEL rules.

Open-ended generation: use a generative model when the output is prose, images, code, or novel reasoning.

One-off judgement: model compilation adds little value to a decision that will not recur.

Unstable outcomes: do not train a classifier when nobody can define or evaluate the correct result.

What you should get back

You should get a short verdict and a ranked list of code-backed opportunities. The answer should name the files or functions behind each recommendation and suggest one sensible first test. It may conclude that Sparkient is not useful.

Cost and implementation

The free trial includes 5,000 one-time credits, one decision type, and 250 total decisions. No card is required.

Developer plan

$19/mo

10,000 monthly credits and two decision types. Decisions, training, generated examples, and hot-model serving draw from the credit balance.

Typical workflow

  1. Define the possible outcomes.
  2. Add or generate labelled examples.
  3. Train and evaluate a model.
  4. Deploy it and call the decision API.