Haiku 5.5 prompting

Haiku 5.5 prompting works best as a task-design exercise: define the answer you need, supply the evidence required to produce it and state how an unacceptable answer will be recognized. Adding more instructions is useful only when those instructions resolve an actual ambiguity or prevent an observed failure.

Begin with one representative task rather than a universal prompt intended to handle every possible user request. A classifier, a rewriting assistant and a document analyst need different contracts. Their prompts can share application policy while keeping their task-specific instructions short enough to inspect and test.

Start with an observable result

For Haiku 5.5 prompting, replace vague quality requests with a concrete deliverable. "Handle this support ticket well" leaves the model to decide whether to classify, answer, escalate or summarize. "Assign one category and quote the sentence supporting it" defines a narrower job that an application can check.

State who will use the output and what decision it supports. A summary for a manager choosing a launch date should preserve blockers and unresolved approvals. A summary for a customer may need different information. Audience context earns its place when it changes the answer's content, not merely its tone.

Include a stopping condition. If the source lacks the required fact, say what the model should return instead of encouraging it to fill the gap. A useful prompt can produce an explicit unresolved result. That result may save more time than an invented answer that looks complete until somebody relies on it.

Separate instructions from source material

Keep the task definition distinct from the text being analyzed. A support ticket can contain demands, quotations or pasted instructions, but those are evidence about the customer's request rather than authority to change your application's behavior. Label the source and describe how it should be used.

Haiku 5.5 prompting cannot replace application security controls. If the model has tools, the server still validates arguments and permissions. A source document saying "approve this refund" does not authorize a financial action, regardless of whether the prompt tells the model to be helpful or to follow customer requests.

Use clear delimiters or structured message fields without treating any delimiter as a security guarantee. The purpose is to reduce ambiguity in the task. Access control, secret handling and execution boundaries remain responsibilities of the surrounding application.

Use a small Haiku 5.5 prompting fixture

The following authored example asks for a constrained customer-support draft. It is not a model-generated answer or a claim that the website can inspect a real account. Copying the sample does not start a request; submitting it in a workspace uses that workspace's normal rules.

Task: Draft a reply to the customer using only the supplied facts.
Audience: A customer waiting for an order.
Facts: The tracking page has not updated for three days. We do not have
the order number and have not checked the carrier or account.
Required content: Acknowledge the delay and ask for the order number.
Constraints: Under 70 words. Do not promise a refund or a delivery date.
Output: The reply only, without an introduction or analysis.
Customer: My order still has not arrived. Can you tell me what happened?

Acceptance is concrete: the reply acknowledges the delay, asks for the missing identifier and makes no unsupported promise. A friendly answer that invents a carrier investigation should fail. A terse answer that meets every factual condition can be improved stylistically without being confused with a factual defect.

Change one source fact to test whether the prompt follows evidence. Supply a verified delivery date in a second fixture and an unknown order status in a third. The response should adapt to those differences instead of repeating a memorized template that ignores the input.

Choose examples that clarify boundaries

Examples are most useful when they distinguish easily confused cases. In a billing classifier, show the difference between a payment failure and a login problem that happens to mention a subscription. Several obvious examples from the same category may add length without teaching the important boundary.

For Haiku 5.5 prompting, keep example outputs aligned with your current schema and policy. An old example containing a retired label can conflict with a newer instruction listing allowed categories. Resolve that contradiction in the prompt source instead of adding another sentence telling the model to follow all instructions perfectly.

Reserve some fixtures for evaluation rather than including every test case in the prompt. Otherwise you can overfit the instructions to the visible examples and learn little about new inputs. Track which examples are demonstrations and which remain independent checks.

Diagnose the failure before revising the prompt

An empty answer may reflect output-budget handling or a non-text response block rather than unclear instructions. A rejected request may contain an unsupported parameter. Inspect the response envelope and stop reason before using prompt wording to solve an integration problem.

Once transport and parsing are correct, classify the task failure. Did the model omit a required fact, invent a claim, choose the wrong label or violate the output format? Each suggests a different change. A general instruction to be more accurate does not explain which distinction the previous prompt failed to convey.

Use the model-specific prompting guide for known behavior patterns. Haiku 5.5 prompting advice should be applied to an observed problem, then tested locally; a vendor recommendation is not a guarantee for every custom workflow.

Keep effort and prompt changes separate

When testing Haiku 5.5 prompting, record the model's effort configuration alongside the prompt version. Changing both at once makes the result difficult to explain. First compare prompt revisions under a fixed configuration, then test whether another effort setting improves the remaining difficult cases.

Do not equate a longer explanation with better reasoning. Score the supported conclusion and task constraints independently from verbosity. A model can produce a detailed rationale for an incorrect label, while a short answer can satisfy the task exactly.

The reasoning guide covers output allowance and effort evaluation. Keep your prompt focused on the deliverable, evidence and required checks. Requests for endless internal deliberation are a poor substitute for a clear definition of success.

Turn revisions into a measured experiment

Give each prompt revision a stable identifier. Run the same evaluation set through both versions and retain individual outcomes, not just an aggregate preference score. A revision that helps the most common case can still introduce a serious regression in a less frequent category.

A practical Haiku 5.5 prompting review records why each change was made. "Added an unknown state after the source omitted the payment date" is more useful than "improved instructions." The explanation lets a future maintainer decide whether the sentence still belongs after the task changes.

Remove instructions that no longer serve a tested purpose. Prompts tend to accumulate patches from old incidents, including rules that conflict or duplicate one another. Review them as maintained application configuration, with owners and regression tests, rather than as a collection of increasingly emphatic reminders.

Keep a small change log for Haiku 5.5 prompting experiments. Record the failed fixture, the instruction changed and whether previously passing cases still passed. Include inconclusive experiments too: a revision that produces mixed results should not be remembered later as an established improvement. When a change improves only one task category, consider a category-specific prompt instead of imposing its extra instructions on every request.

Know when prompting is not the next fix

If the task needs a fact outside the supplied material, add an authorized retrieval path or ask for the missing source. If the output must satisfy a machine-readable contract, use structured output and application validation. If an action requires permission, enforce that permission in code.

Haiku 5.5 prompting remains valuable within those boundaries. Use the text workspace for a synthetic trial, then judge the answer against written criteria rather than whether it sounds impressive. The strongest prompt revision is one whose benefit you can explain with a specific failure it prevents.