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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Many prompting habits that once seemed adequate can leave Claude guessing about the task, the desired result, or whether it should act. Anthropic’s current Prompting best practices documentation says Claude responds well to clear, explicit instructions. The 19-item checklist below is an editorial audit based on that guidance—not an official Anthropic list or a measured ranking of mistakes.
Make the task and the result unmistakable
1. Asking vaguely
“Help with this” leaves the task open to interpretation. Name the action you want, such as summarizing, extracting, comparing, rewriting, or classifying. Anthropic recommends clear, direct instructions rather than relying on Claude to infer the task.
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2. Leaving success criteria unstated
A task can be clear but still underspecified. Say what a satisfactory result must contain or accomplish—for example, “identify the three largest risks and support each with a source passage.” That gives Claude a target to meet instead of a broad subject to discuss.
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3. Omitting why the work matters
Relevant motivation can help tailor an answer to its purpose. Explain the intended use, audience, or workflow when it changes what a useful result looks like, such as whether a summary is for an executive decision or a technical handoff.
4. Expecting Claude to infer the audience
Specify who will read or use the output and what they already know. “Explain this for a new support agent” is more actionable than “make it accessible,” because it anchors the level of explanation to a concrete reader.
5. Asking for an unspecified level of detail
Tell Claude whether you want a concise answer, a thorough explanation, or a particular scope. Without that direction, a response may be too brief to use or longer than the task warrants.
Define the output before Claude writes it
6. Failing to name the format
If the result must be a table, checklist, email, JSON object, or set of steps, ask for that format explicitly. A topic alone does not tell Claude how the answer needs to fit into the next stage of your work.
7. Leaving structure and constraints implicit
State useful boundaries: required sections, word or item limits, fields to include, or things to exclude. Constraints should be specific enough to guide the output without contradicting one another.
8. Blending instructions with the material to process
Make it easy to tell what Claude should do and what it should work on. For complex prompts, separate the task, background, examples, and source material with descriptive labels or XML tags such as <instructions>, <context>, and <document>. Anthropic recommends XML tags as a way to clarify these boundaries.
Use examples and long inputs deliberately
9. Giving no example when consistency matters
When format, tone, or a recurring judgment is hard to describe, show a representative input and desired output. Examples can make abstract preferences easier to follow.
10. Using examples that do not match the real task
An example only helps if it reflects the kind of material and result you actually need. Use relevant examples rather than polished samples that differ from the real workflow.
11. Showing only one narrow case
If real inputs vary, include examples that represent meaningful variation or edge cases. Anthropic’s guide recommends 3–5 examples for best results; treat that as documentation guidance, not a guarantee or a universal optimum.
12. Putting a long source after the question
For long-document work, Anthropic advises placing the source material near the top and the query after it. The guide says this ordering improved response quality by up to 30 percent in its tests, especially with complex, multi-document inputs. That figure is limited to those tests; it should not be read as a promise for every prompt.
13. Sending a large, unstructured input
Organize long material into clearly marked documents and include useful metadata, such as a document title or date, when it helps distinguish sources. Anthropic’s long-context recommendations address inputs of 20k+ tokens; that threshold identifies the scope of the guidance, not a rule that every larger prompt needs identical treatment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Be explicit about action and model-specific behavior
14. Asking for ideas when you want implementation
Distinguish advice from action. If you want Claude to edit a file, run a tool, or carry out a task, say so directly and provide the relevant constraints. A request for suggestions can reasonably produce suggestions instead of implementation.
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Name the tool or action you expect Claude to use when that matters, and make clear whether it should proceed or only explain what it would do. Tool availability depends on the environment; a prompt cannot grant a capability that has not been provided.
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16. Reusing model-specific instructions without checking them
Some prompting advice concerns particular model generations or features. When switching models or adapting an old prompt, check Anthropic’s current guidance rather than assuming every behavior and recommendation carries over unchanged.
17. Using blanket behavioral demands
Broad commands such as “never make mistakes” do not define a practical standard. Replace them with targeted directions: what to verify, what sources to use, and how to handle uncertainty. Anthropic’s guide discusses overtriggering and overengineering in some model contexts, so sweeping behavioral demands can be counterproductive.
18. Treating forceful wording as a substitute for precision
Repeating an instruction emphatically does not resolve missing context, conflicting constraints, or an unclear deliverable. Identify the specific failure you want to prevent and state the corresponding check or boundary.
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19. Assuming one prompt is optimal everywhere
Prompt quality depends on the task and model context. Treat general principles—clear instructions, relevant context, useful examples, and well-defined output—as a starting point, then assess the result in the workflow where the prompt will actually be used. Keep model-specific recommendations distinct from general ones.
Quick Recap
A quick prompt audit
- Task: Is the requested action explicit?
- Purpose: Does Claude have the relevant audience and workflow context?
- Success: Is the expected result defined?
- Output: Are format, structure, scope, and constraints stated?
- Inputs: Are instructions, examples, and source material clearly separated?
- Long context: Are sources organized before the query?
- Action: Is it clear whether Claude should advise or use an available tool to implement?
- Model: Have you checked whether any model-specific instruction still applies?
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