The 5 Reasons Claude Gives You Different Feedback Categories Every Time in 2026

Paste 500 support tickets into Claude and ask it to categorize them. Do it again tomorrow with the same tickets and the same prompt. You will get a different set of categories. Not wildly different, but different enough that you cannot put the two runs on the same chart, and different enough that the trend line you were trying to build does not exist.

This is not a flaw in Claude. It is what a stateless model does when asked to invent a structure, and Claude is very good at the part it is actually being asked to do. The five reasons the categories move are token-level sampling, no persistent definitions between runs, batch-local category invention, prompt sensitivity, and the absence of any correcting loop. Understanding which of the five is biting you determines whether it is fixable with better prompting or whether you have hit the actual limit.

The 5 reasons Claude gives you different feedback categories every time

1. Token-level sampling

Generation is probabilistic. At default settings the model samples from a distribution rather than always taking the single likeliest next token, which means two identical requests can diverge from the first sentence onward. Once the first category is phrased "billing confusion" instead of "billing issues," everything downstream sorts differently.

Mitigation that works: lower the temperature. This reduces variance and does not eliminate it, and it does nothing about the other four reasons.

2. No persistent definitions between runs

When you ask for categories, the model invents them in the moment from the text in front of it. Nothing carries over. Yesterday's decision that "slow export" and "export timeout" are one category or two is not stored anywhere, so today it gets re-decided from scratch.

This is the reason most teams misdiagnose. It looks like inconsistency and it is actually statelessness. The model is not forgetting your scheme; you never gave it one to keep.

Mitigation that works: supply the category list explicitly in every prompt and instruct the model to classify into it rather than invent. This is a genuine fix for consistency, and it converts your problem into a different one: now you are maintaining a category list by hand.

3. Categories are invented per batch, not per corpus

Claude sees what fits in the context window. If you process feedback in batches of 500, each batch produces categories that describe that batch. Batch one, arriving in January, invents categories for January's problems. Batch nine, in March, invents categories for March's.

The result is a taxonomy that quietly re-bases itself as you go, so a theme that looks like it grew 40% may have grown zero and simply been named more specifically in later batches.

Mitigation that works: define the scheme once against a representative sample, then classify everything against it. Which again means owning a scheme.

4. Prompt sensitivity

"Categorize these tickets by theme" and "group these tickets by the underlying problem" produce measurably different granularities. So does the order of examples, the number of categories you suggest, and whether you ask for a flat list or a hierarchy. Small wording changes reshape the output more than most people expect, which makes informal prompt edits a hidden source of drift.

Mitigation that works: version your prompt and treat changes to it as a schema migration, because that is what they are.

5. Nothing corrects the scheme over time

The deepest one. In a manual workflow, a human notices that a category has become useless and splits it. In an automated pipeline with a fixed list, nothing notices. In an ad hoc Claude workflow, the model re-invents rather than corrects, so errors do not accumulate but neither does learning. There is no mechanism by which the categorization gets better at describing your product.

Mitigation that works: none, at the prompt level. This is architectural.

The mitigations converge on the same answer

Read the five fixes together and a pattern emerges. Lower the temperature, supply the categories, define the scheme against a representative sample, version the prompt. Four of the five practical mitigations are variations of one instruction: stop letting the model invent the structure and give it a structure to apply.

Which is correct, and it relocates the work rather than removing it. You now own a category list, its edge cases, its revisions when the product ships something new, and the migration every time you change it. Enterpret's research found teams spending 6 to 8 hours a week on exactly that maintenance. The LLM made classification cheap and left the expensive part, deciding and maintaining what the categories are, entirely with you.

That is the honest state of the tooling, and it explains a specific pattern: teams get an excellent first result from Claude, present it, get asked for the same analysis next month, and discover the second run is not comparable to the first. The demo works. The trend line does not.

The alternative is a system where the categories are derived from the full corpus rather than a batch, persist across runs, and revise themselves as the product changes rather than being re-invented or manually migrated. That is what an adaptive taxonomy does, and it is why the same feedback categorized twice produces the same answer. Pair it with a customer context graph and each theme also carries the account, segment, and revenue behind it, which is context a model cannot infer from ticket text no matter how well you prompt it.

None of this makes Claude the wrong tool. It makes it the wrong layer to put your taxonomy in. See how to use Claude for customer feedback analysis and best Claude prompts for analyzing customer feedback.

What Claude is genuinely better at

Worth being specific, because the failure mode above is narrow and the strengths are real.

Claude is excellent at one-off synthesis where reproducibility does not matter: reading a churn interview and pulling the argument out, summarizing a week of tickets for a standup, drafting the narrative around a finding you already have. It is excellent at exploration, where you want to know what is in a corpus you have never read. And through MCP it is very good as the interface to a structured feedback corpus, asking questions in natural language against categories that were established elsewhere.

The distinction is whether the output needs to be comparable to a previous output. Analysis you will act on once: use Claude directly. Analysis you will trend, report, or defend in a prioritization meeting: the categories need to live somewhere stateful.

Decision rule: if anyone will ask "is this going up or down," do not let the model invent the categories.

FAQ

Why does Claude give different answers to the same feedback question?

Generation samples probabilistically, so identical prompts can diverge, and more importantly the model holds no state between sessions. Each run re-invents the category scheme from whatever text is in the context window rather than applying a scheme it remembers, so the output is fresh rather than consistent.

How do I get consistent categorization from an LLM?

Supply the categories explicitly and instruct the model to classify into them rather than generate them, lower the temperature, and version your prompt like a schema. That gets you reproducibility and hands you the ownership of the category list, its edge cases, and every migration, which is where the recurring cost lives.

Can Claude replace a customer feedback platform?

For exploration and one-off synthesis, it is often better than a platform. For anything you need to trend over time, compare across months, or tie to specific accounts and revenue, it is missing the two things that make those possible: a persistent category scheme and a join to your customer records.

How does Enterpret solve the consistency problem?

Its adaptive taxonomy derives categories from your full feedback corpus rather than from whatever fits in a context window, persists them across runs so the same input produces the same output, and revises them as your product changes instead of requiring a manual migration. The customer context graph then attaches the account, segment, and revenue behind each theme, which no amount of prompting can extract from ticket text alone.

Should we use Claude and a feedback platform together?

That is the common setup and it works well. The platform holds the structure and the customer context; Claude is the interface for asking questions against it, often over MCP. You get natural-language exploration on top of categories that stay put, which is the combination neither delivers alone.

If your second Claude run does not match your first, see how the adaptive taxonomy keeps categories stable.

‍