Migrating Prompts From Claude/GPT To Hermes: Gotchas

Most prompts that work on Claude or GPT need adjustment to work well on Hermes. Knowing what to change — and what not to bother with — saves a week of trial and error.

9 min · Reviewed 2026

What porting really involves

A prompt is not pure logic — it is logic plus model-specific phrasing that the original model is good at following. Migrating it is part translation, part re-tuning, part discovering which parts of the original were actually doing work. Plan for each prompt to need a few rounds of iteration before it matches the source-model output.

Common gotchas, by source model

Source model trait	What it depends on	Hermes adjustment
Claude XML tags (<thinking>, <answer>)	Anthropic's tag conventions	Replace with plain prose sections and explicit format directives
Claude polite-and-thorough tone	Anthropic tuning	May come out shorter and blunter; tune system prompt for length
GPT step-by-step reasoning instructions	OpenAI's chain-of-thought training	Works on Hermes but may need explicit 'think step by step' more often
GPT JSON mode reliance	OpenAI strict-json infrastructure	Replace with grammar-constrained decoding or schema-with-examples
GPT tool-call format	OpenAI's tools schema	Convert to Hermes's documented function-call grammar
Memory / Custom Instructions	OpenAI's persistent state	Move stable parts into Hermes system prompt; rebuild the rest

Things that usually port for free

Plain task descriptions ('summarize this article in 100 words').
Schema-locked output requests with one example.
Anti-rules ('do not include code fences', 'do not preamble').
Domain-specific glossaries and definitions.
Few-shot examples — these often help Hermes more than they helped the source model.

Things that need real work

Tool-using prompts — different grammar, different harness expectations.
Long persona prompts — Hermes responds in kind, sometimes more than you wanted.
Anything depending on memory or persistent state — you build the state layer yourself.
Anything depending on a specific provider feature (web search, image gen, code interpreter) — those become separate components.

Migration workflow

Pick the highest-value prompt in your library.
Snapshot its current output on the source model for 10 representative inputs.
Translate to Hermes using the gotchas table.
Run on the same 10 inputs. Compare line by line.
Iterate on the system prompt until Hermes outputs are equivalent or you accept the gap.

Applied exercise

Open one prompt that you know works well on Claude or GPT.
Identify every line that depends on a model-specific feature using the gotchas list.
Rewrite those lines for Hermes.
Run on three sample inputs and compare. Note which adjustments mattered most for your use case.

The big idea: porting prompts is a translation, not a copy. Mind the gotchas, evaluate honestly, and trim what is no longer carrying weight.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-hermes-prompt-migration-creators

What is the core idea behind "Migrating Prompts From Claude/GPT To Hermes: Gotchas"?
1. Most prompts that work on Claude or GPT need adjustment to work well on Hermes. Knowing what to change — and what not to bother with — saves a week of trial and error.
2. safety tuning
3. dashboard
4. Compare your scores to the table. Note where it agrees with what you're doing no…
Which term best describes a foundational idea in "Migrating Prompts From Claude/GPT To Hermes: Gotchas"?
1. tool grammar
2. prompt migration
3. tone calibration
4. regression test
A learner studying Migrating Prompts From Claude/GPT To Hermes: Gotchas would need to understand which concept?
1. prompt migration
2. tone calibration
3. tool grammar
4. regression test
Which of these is directly relevant to Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. prompt migration
2. tool grammar
3. regression test
4. tone calibration
Which of the following is a key point about Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. Plain task descriptions ('summarize this article in 100 words').
2. Schema-locked output requests with one example.
3. Anti-rules ('do not include code fences', 'do not preamble').
4. Domain-specific glossaries and definitions.
Which of these does NOT belong in a discussion of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. Plain task descriptions ('summarize this article in 100 words').
2. Schema-locked output requests with one example.
3. Anti-rules ('do not include code fences', 'do not preamble').
4. safety tuning
Which statement is accurate regarding Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. Long persona prompts — Hermes responds in kind, sometimes more than you wanted.
2. Anything depending on memory or persistent state — you build the state layer yourself.
3. Tool-using prompts — different grammar, different harness expectations.
4. Anything depending on a specific provider feature (web search, image gen, code interpreter) — those …
Which of these does NOT belong in a discussion of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. safety tuning
2. Long persona prompts — Hermes responds in kind, sometimes more than you wanted.
3. Tool-using prompts — different grammar, different harness expectations.
4. Anything depending on memory or persistent state — you build the state layer yourself.
What is the key insight about "The eval-set discipline saves you" in the context of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. Build a 30-50 prompt eval set BEFORE migrating. Run it on the source model and snapshot the outputs.
2. safety tuning
3. dashboard
4. Compare your scores to the table. Note where it agrees with what you're doing no…
What is the key insight about "Don't keep the source-model phrasing if it's not doing work" in the context of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. safety tuning
2. Sometimes a prompt has 'You are a careful assistant who...' purely because the original author copied it from a tutorial.
3. dashboard
4. Compare your scores to the table. Note where it agrees with what you're doing no…
What is the key insight about "From the community" in the context of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. safety tuning
2. dashboard
3. On r/LocalLLaMA, the migration gotcha that catches almost everyone is implicit instructions: prompts written for proprie…
4. Compare your scores to the table. Note where it agrees with what you're doing no…
Which statement accurately describes an aspect of Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. safety tuning
2. dashboard
3. Compare your scores to the table. Note where it agrees with what you're doing no…
4. A prompt is not pure logic — it is logic plus model-specific phrasing that the original model is good at following.
What does working with Migrating Prompts From Claude/GPT To Hermes: Gotchas typically involve?
1. The big idea: porting prompts is a translation, not a copy. Mind the gotchas, evaluate honestly, and trim what is no longer carrying weight.
2. safety tuning
3. dashboard
4. Compare your scores to the table. Note where it agrees with what you're doing no…
Which best describes the scope of "Migrating Prompts From Claude/GPT To Hermes: Gotchas"?
1. It is unrelated to model-families workflows
2. It focuses on Most prompts that work on Claude or GPT need adjustment to work well on Hermes. Knowing what to chan
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Migrating Prompts From Claude/GPT To Hermes: Gotchas?
1. safety tuning
2. dashboard
3. Common gotchas, by source model
4. Compare your scores to the table. Note where it agrees with what you're doing no…

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