ChatGPT Not Following Instructions? Here’s How to Get Precise Answers

Understanding why ChatGPT might not interpret your request correctly

Most issues happen due to how the request is framed, not just the behavior of the model. Common reasons include:

• Stacked goals in one message. The model may select one goal and omit the others.

• Undefined terms. Words like “enterprise” or “qualified” might have different meanings for different teams or contexts.

• Missing constraints. Inputs may overlook factors like audience, market, price, or legal rules.

• Format drift. You want JSON, but don’t specify “JSON only.”

• Conflicts. Requests may include contradictory instructions such as asking for a neutral tone and persuasive claims simultaneously.

• Long threads. Earlier context can get lost. Starting a new conversation helps narrow the scope.

• Outdated knowledge. Not requesting sources can result in outdated or incomplete information.

Why writing a succinct and detail-oriented task brief yields better results

Instead of a chatty question, frame your request as a clear, testable brief, much like a ticket in your internal system.

Core elements for any business task

• Goal: One clearly defined outcome in a single sentence.

• Audience and use case: Specify who will use the output and why.

• Inputs provided: Attach data or provide a short summary.

• Constraints: Region, industry rules, budgets, and deadlines or expected delivery dates.

• Format: Specify the required structure, fields, and measurement units.

• Style rules: Specify words to avoid and share examples to follow.

• Acceptance tests: Define how you’ll assess if the output meets requirements.

For project management

• Define scope and phase. Example: “Phase: execution; Method: Scrum.”

• Ask for fields such as risk, owner, status, and due date.

• Discourage filler like “ensure alignment.” Request specific, actionable items.

For knowledge management

• Establish taxonomy and provide a list of accepted tags and definitions.

• Specify source citations and how links should be formatted.

• Limit entry length. Example: 120–150 words per entry.

For CRM and sales

• Set clear ICP (ideal customer profile) criteria and disqualifiers.

• Define fixed stage names and exit rules. Example: “SQL requires ARR estimate.”

• Require fields such as account, region, use case, next step, and owner.

Specify outputs with formats that both humans and systems can validate

Be explicit about how the result should be presented. This makes parsing and verifying easier.

1. Use JSON for integrations and quality assurance.

2. Utilize structured tables for document sharing.

3. Define permissible values for every field.

1. Plan. Have the model request any clarification or missing input before producing results.

2. Draft. Generate an initial output in the required format.

3. Verify. Run acceptance tests and resolve any discrepancies or gaps.

Acceptance tests: Output uses US spelling; status is {open, watch, mitigating, closed}; no sentence over 20 words; include owner for each line; provide one mitigation per risk.

Implement guides or standards that maintain consistency across your team

• Role first. Begin every brief with the domain or intended role.

• Banned terms. List phrases to exclude from results.

• Numerical limits. Specify budgets, maximum word counts, and quantity requirements.

• Schema locks. Provide a fixed schema and prohibit extra fields.

• Inline examples. Include a small, labeled sample input-output pair.

• Source rules. Instruct the model to cite links or state “no source” if the information is unknown.

If a value is unknown, write null. Do not guess.

If ChatGPT deviates from your instructions, try this reset routine

1. Summarize back. Ask the model to restate your main goal in a single sentence.

2. Pin the format. Restate the required schema and forbidden elements.

3. Narrow scope. Remove any extra objectives so only one output is requested.

4. Reset. Start a fresh chat and provide the brief again.

5. Test. Add two clear acceptance checks and rerun the prompt.

6. Escalate. Share a small, specific example the model should emulate.

Centralize instructions to ensure consistency across systems

Store approved briefs, schemas, and acceptance checks in one workspace to prevent drift across your teams and tools.

Select a hub that supports projects, documentation, and CRM needs. Many teams use Routine, Notion, or monday.com. For a comparative look at options, see this guide on the differences between all-in-one workspaces and specialized project tools and how these choices impact team standardization.

Sample templates you can adapt to your environment

Project risk summary (for weekly reviews)

• Goal: Condense new risks into a six-column table.

• Inputs: Last week’s risks and new incident notes.

• Constraints: US market, no financial advice, one owner per risk.

• Format: risk_id, cause, impact, owner, mitigation, status.

• Acceptance tests: Status uses allowed list only; max 150 words total.

Knowledge base consolidation (for duplicated entries)

• Goal: Merge duplicate articles into a single canonical entry.

• Inputs: Two article bodies and accompanying metadata.

• Constraints: Retain the older URL slug; include a list of redirects.

• Format: Title, summary, canonical URL, obsolete URLs[], tags[].

• Acceptance tests: At least three tags; summary ≤ 120 words.

Sales qualification rewrite (for CRM hygiene)

• Goal: Transform messy notes into a structured CRM record.

• Inputs: Original call transcript snippets.

• Constraints: Adhere to ICP rules; redact any PII; next step must be within 7 days.

• Format: account, use_case, blockers[], budget_range, next_step, owner.

• Acceptance tests: Set budget to null if unknown; avoid adjectives without supporting facts.

Precision isn’t luck, it's process. Standardize your process, and accuracy will consistently follow.

FAQ

Why do models sometimes provide incorrect outputs?

Models often fail due to poorly framed requests, stacked objectives, or conflicting instructions. Clarity and precision are vital; vague, loaded, or contradictory inputs derail the process.

How can I ensure my request is interpreted correctly by models?

Define a singular goal with precise parameters and constraints. Avoid unnecessary complexity and set the stage for a focused output.

What role does format specification play in successful outputs?

Format specification is crucial for both human and system validation. Without it, results can stray, leading to inefficiencies and additional verification tasks.

How can I manage outdated or incomplete information in responses?

Demand source citations for data accuracy and relevance. This mitigates risks of outdated insights influencing critical decisions.

What strategies improve the iterative process with AI models?

Adopt a multi-step workflow: planning, drafting, and verification together fortify the reliability and quality of your end output. Hasty single-step approaches amplify errors and necessitate reworks.