Free AI prompt checker and analyzer

The Intelligence Score grades the clarity, precision and context density of your prompt from 0 to 100, with specific fixes, before you run it in ChatGPT, Claude or Gemini.

Evaluation Benchmark

AI-graded across clarity, precision, and density

Prompt Benchmarking & Heuristic Evaluation

What is an AI Prompt Intelligence Score and Why Should You Benchmark?

Prompt engineering is not guesswork—it is a discipline grounded in how transformer neural networks prioritize tokens. When a prompt lacks structural clarity or negative boundaries, the AI model produces average, generic completions.

Intelligence Score evaluates your prompt against proven heuristic dimensions (Clarity, Constraint Precision, and Signal Density) to output a 0–100 benchmark. By grading your instructions before running them in ChatGPT, Claude, or Gemini, you can catch vague wording, missing constraints and filler before you spend a request.

The 3 Pillars of Prompt Intelligence

Linguistic Clarity

Measures the absence of ambiguous pronouns, vague adjectives, and conflicting directives. Clear prompts reduce attention drift across model layers.

Constraint Precision

Evaluates the presence of explicit negative constraints, numerical bounds, output schemas, and non-negotiable rules.

Contextual Signal Density

Calculates the ratio of high-value domain instructions versus conversational fluff and pleasantries that waste tokens.

Intelligence Score Quality Tiers

Score Range	Classification	Expected AI Behavior
0 – 49	Needs Optimization	High risk of hallucinations, generic responses, and ignored formatting
50 – 74	Acceptable (Needs Polish)	Functional for simple tasks, but prone to edge-case errors in complex workflows
75 – 89	Strong	High accuracy, clear boundaries, and structured output formatting
90 – 100	Production-Grade	Rock-solid, deterministic execution suitable for autonomous agents and APIs

Explore Related Prompt Engineering Tools

Prompt Optimizer Prompt Debugger Token Optimizer Prompt Cookbook

What the Intelligence Score does

The Intelligence Score is Cuelara's free AI prompt checker and prompt analyzer. It grades a prompt from 0 to 100 on three dimensions: clarity (is the instruction unambiguous), precision (does it set boundaries and an output format) and context density (how much of the text is useful detail rather than filler). You get the overall score, a short rationale for each dimension and a ranked list of specific fixes.

An AI model reads your prompt and scores each dimension; the overall score is then calculated from those three with fixed weights (35% clarity, 40% precision, 25% density). Use it to answer "is this prompt good enough to send?" before you spend a request or ship a prompt in a product. To rewrite a weak prompt, use the prompt optimizer. To find out why a prompt fails, use the prompt debugger. To start from an idea, use the prompt builder.

How to use the Intelligence Score

1
Paste your prompt
Any prompt works, from one line to a full system prompt.
2
Pick a mode and a target
Choose how strictly to judge it (Standard, Strict for production, or Creative) and whether to score it for any model, GPT-4 or Claude 3.
3
Get your score
You see the overall 0 to 100 score and its tier, plus a score and rationale for clarity, precision and context density.
4
Fix and score again
Work through the suggestions, which are marked high, medium or low priority, then re-score and watch the number move.

Example: before and after

Before

Write a blog post about dogs.

After

Overall: 24/100 (Needs Optimization)
- Clarity: low. No audience, angle or purpose.
- Precision: very low. No length, tone, structure or output format.
- Context density: low. Almost no detail to work from.
Top fix: say who the post is for, how long it should be and what the reader should do afterwards.

Illustrative output. Real scores vary with the prompt and the mode you pick. A score makes the gap concrete: it is easier to fix a prompt when you can see which part is weak.

Who the Intelligence Score is for

Prompt writers
Check quality quickly and track improvement.
Teams
Agree on a minimum score for prompts that go into a product.
Learners
See what separates a weak prompt from a strong one.
Developers
Review prompts before they are deployed.

Intelligence Score FAQ

What is an AI prompt checker?

An AI prompt checker reviews a prompt before you send it and tells you how well it will communicate with a model. The Intelligence Score does this with a 0 to 100 score, a rationale for each dimension and specific fixes.

How is the prompt score calculated?

An AI model rates the prompt on clarity, precision and context density. The overall score is then worked out from those three ratings with fixed weights: 35% clarity, 40% precision and 25% density. Because a model does the rating, the same prompt can move by a few points between runs.

What is a good prompt score?

Below 50 is Needs Optimization, 50 to 74 is Acceptable, 75 to 89 is Strong and 90 or more is Production-Grade. Aim for 75 or more for prompts that matter. A high score means the prompt leaves little to guess; it does not guarantee a good answer, because the model's output is not scored.

How do I raise a low prompt score?

Add explicit constraints and an output format, remove filler such as "please can you help me", and give the model concrete detail. The suggestions tell you which is missing. The prompt optimizer can do the rewrite for you.

How is this different from the Prompt Debugger?

Both check a prompt. The Intelligence Score gives you a number and a breakdown, so you can measure and compare versions. The prompt debugger diagnoses a prompt that is already failing, listing vague wording, contradictions and missing constraints with a fix for each.

Is the AI prompt checker free?

Yes. You can use it free within a daily limit, which is higher when you sign in and higher again on the paid plans.

Prompts to try it on

Browse the Prompt Cookbook

Data Analysis & Data Science

ChatGPT Prompt to Check Data Quality Before Starting Analysis

This data quality check prompt is built for anyone who needs to validate a dataset before running any real analysis on it: analysts, data sc…

ROLE: You are a data quality analyst reviewing a dataset before it is used for analysis.

CONTEXT:
Dataset description: [WHAT THE DATASET CONTAINS AND ITS SOURCE]
Intended use: [WHAT ANALYSIS OR DECISION THIS DATA WILL SUPPORT]
Columns and expected types/ranges: [LIST COLUMN NAMES WITH EXPECTED FORMAT, e.g. "signup_date: YYYY-MM-DD", "amount: positive decimal"]
Data sample or full dataset: [PASTE DATA OR DESCRIBE WHERE IT IS ATTACHED]

TASK:
Review the dataset against these quality dimensions:
1. Completeness - identify missing or null values and which columns/rows they affect
2. Consistency - flag mixed formats, inconsistent casing, or conflicting values for the same entity
3. Validity - flag values outside expected ranges, types, or formats
4. Uniqueness - identify duplicate records or duplicate keys that should be unique

CONSTRAINTS:
- For each issue found, cite the specific column and example row(s), not just a general statement
- Rate each issue's severity as Blocking, Should Fix, or Cosmetic based on impact to [INTENDED USE]
- Do not attempt to fix the data yourself; only identify and explain issues
- If you cannot determine whether something is an issue without more context, say so explicitly rather than guessing

OUTPUT FORMAT:
A table with columns: Issue, Column(s) Affected, Example Row(s), Severity, Why It Matters
Followed by a short summary paragraph stating whether the dataset is ready for analysis or needs cleanup first.

Data Analysis

AI Prompt to Audit a Notion Database for Data Quality Issues via Notion's MCP Server

This Notion MCP prompt is for operations, data, and RevOps teams who keep a working database in Notion (a CRM tracker, inventory log, projec…

You are a data quality analyst reviewing a live database retrieved via an MCP connection to Notion.

Context:
- Database name: [DATABASE_NAME]
- Approximate number of records: [RECORD_COUNT]
- Required properties that must always be filled in: [REQUIRED_PROPERTIES]
- Properties with a fixed set of intended values (e.g. Status should only be one of a known list): [CONTROLLED_PROPERTIES_AND_VALUES]

Task:
Retrieve the records in this database and check them for the following issues:
1. Missing values in required properties
2. Duplicate records (same entity represented more than once)
3. Inconsistent formatting or casing in controlled properties
4. Broken or empty relation fields
5. Date fields that are empty, in the past when they shouldn't be, or otherwise clearly incorrect

Constraints:
- Only flag issues you can verify from the retrieved data; do not guess at values you cannot see
- Group findings by issue type, then by severity (Blocking, Needs Review, Cosmetic)
- Include the page title or unique identifier for every flagged record so it can be located in Notion

Output format:
Return a structured report with one section per issue type. Each section should list affected records as a short table: [Record Name | Issue Description | Suggested Fix]. End with a one-paragraph summary of total issues found and the top priority to address first.

Marketing & Sales

AI Prompt to Score and Prioritize Sales Leads Using HubSpot's MCP Server

This AI prompt for HubSpot's MCP server is built for sales and marketing teams who want to turn a crowded HubSpot pipeline into a ranked lis…

ROLE
You are a sales operations analyst with access to HubSpot through an MCP (Model Context Protocol) connection. You can call HubSpot MCP tools to retrieve contact, company, and deal records.

CONTEXT
Company: [COMPANY NAME]
Target segment: [TARGET SEGMENT, e.g. mid-market SaaS companies]
Lead source(s) to review: [LEAD SOURCE, e.g. "contacts created in the last 14 days" or "Marketing Qualified Leads list"]
Sales motion: [SALES MOTION, e.g. inside sales, outbound SDR, self-serve trial]

SCORING RUBRIC
Score each lead from 0-100 using these weighted factors:
- Firmographic fit ([WEIGHT 1]%): company size, industry, and tech stack match against [IDEAL CUSTOMER PROFILE]
- Engagement recency ([WEIGHT 2]%): email opens/clicks, site visits, and form submissions in the last [TIME WINDOW]
- Deal stage and value ([WEIGHT 3]%): current HubSpot deal stage and estimated deal value
- Buying-stage signals ([WEIGHT 4]%): explicit intent such as demo requests, pricing page visits, or replies to outreach

TASK
1. Use HubSpot MCP tools to retrieve the contact, company, and deal records for the lead source specified above.
2. Apply the scoring rubric to each lead using the actual field values returned by HubSpot, not assumptions.
3. Rank the leads from highest to lowest score.
4. For each of the top [NUMBER OF LEADS] leads, state the score, a one-line reason citing the specific HubSpot data that drove it, and a recommended next action.

CONSTRAINTS
- Do not assign a score without first retrieving the underlying HubSpot record through an MCP tool call.
- If a required field is missing from HubSpot, flag it explicitly instead of guessing a value.
- Keep the final list to [NUMBER OF LEADS] leads maximum.

OUTPUT FORMAT
A ranked table with columns: Rank | Lead Name | Score | Key Reason | Recommended Next Action
Followed by a short summary of any leads that had missing or incomplete HubSpot data.