Magic LibraryMagic AI, Magic Agents, and Super Magic run on Claude models hosted on AWS Bedrock. The techniques on this page come from Anthropic’s own prompt engineering guidance and apply directly to any prompt you write inside Thread — Triage Agent intents, recap templates, categorization rules, priority rules, Magic Title rules, and Super Magic responses.For the fundamentals, see Best practices for writing Magic AI prompts. This page is the companion for when the fundamentals aren’t producing the quality you need.
Golden rule: write for a brand-new hire
Anthropic’s guidance for writing to Claude is to picture a brilliant new employee who has no context on your MSP, no clue what your PSA looks like, and no sense of what “urgent” means in your world. Every detail you’d tell that person on day one belongs in the prompt — the company terminology, what counts as an outage, which team owns what, and what “good” looks like for the output. If you handed your prompt to a coworker who had never seen the ticket, would they know what to do? If not, Magic AI won’t either.1. Be explicit — tell it what to do, not what not to do
Vague instructions produce generic output. Explicit instructions produce the output you actually want. Less effective- Lead with the action verb. “Summarize”, “Classify”, “Escalate”, “Rewrite” — put it at the front so the intent is unmistakable.
- Tell Magic AI what “good” looks like. Length, tone, structure, what to include, what to omit. Don’t rely on the model to guess.
2. Add context and motivation
Explaining why a rule exists lets Magic AI generalize to cases you didn’t spell out. A rule with reasoning attached holds up better than a bare instruction. Less effective3. Use examples — show, don’t just tell
Examples are one of the most reliable ways to steer Magic AI’s output format, tone, and structure. This is sometimes called few-shot prompting, and it’s especially useful for recap templates, categorization, and title rules where the desired output has a specific shape. Good examples are:- Relevant — they mirror the real tickets you see.
- Diverse — they cover the common edge cases, not just the happy path.
- Structured — wrap each example in tags so Magic AI can tell examples apart from instructions.
4. Structure complex prompts with XML tags
Once a prompt mixes multiple types of content — instructions, background context, examples, ticket data — plain paragraphs get ambiguous. Wrap each type in a descriptive XML tag so Magic AI can parse the boundaries.- Use consistent, descriptive tag names across your prompts.
- Nest tags when content has natural hierarchy (
<examples>wrapping multiple<example>blocks). - Tag names should describe the content, not the format (
<company_context>, not<section_2>).
5. Give step-by-step instructions when order matters
For decisions where the order of checks changes the outcome — triage flows, escalation logic, categorization with fallbacks — write the steps as a numbered list. Magic AI follows structured procedures better than it infers them from prose.6. Ask Magic AI to think before it answers
For decisions that need reasoning — a nuanced priority call, a categorization with overlapping options, a sentiment read — telling Magic AI to reason step by step before committing to an answer measurably improves quality. Anthropic calls this chain-of-thought prompting. Two ways to structure it: Basic — just ask for reasoning:7. Give it permission to say “I don’t know”
Magic AI will try to be helpful even when the ticket doesn’t contain enough information — and that’s when hallucinations creep in (a made-up asset name, an invented user, an assumed office location). Give it an explicit exit:8. Match the style of your prompt to the style you want back
Magic AI’s output tends to mirror the style of the prompt. If you write your prompt in dense paragraphs, expect dense paragraphs back. If you want short, scannable bullets, write the prompt in short, scannable bullets. Practical consequences:- Recap prompts that use markdown produce markdown recaps. If you want a recap in plain sentences, remove the markdown scaffolding from the prompt.
- Title prompts that use ALL CAPS in examples produce ALL-CAPS titles. Match your examples to the casing you actually want.
- Casual prompts produce casual replies; formal prompts produce formal replies. Set the register in the instructions, and reinforce it in your examples.
9. Iterate — the first prompt is never the best one
The best prompts are refined over 5–10 revisions, not written in one shot. A simple loop:1
Write the shortest version that could work
Start with a plain-language instruction and one example. Don’t over-engineer up front — you don’t yet know where it breaks.
2
Test on real tickets from your PSA
Not made-up examples. Run it against 10–20 recent tickets that cover the range of scenarios you actually see.
3
Find the failures, not the successes
Where did Magic AI produce the wrong category, the wrong priority, the wrong tone? Those failures tell you what the prompt is missing.
4
Target the failure — not general 'be better' language
Vague fixes (“be more careful”) don’t work. Concrete fixes (“if the ticket mentions Outlook AND crashing, the sub-type is Email, not Applications”) do.
5
Retest and repeat
Keep going until you can’t find a failure mode on real tickets. Then run it for a week and check again — new tickets will surface new gaps.
10. Use another AI to help you write prompts
Anthropic explicitly recommends using an assistant (Claude, ChatGPT, or Magic AI itself) as a prompt-writing partner. You describe the goal in plain language and ask it to draft a prompt, generate additional examples, or point out ambiguity in your draft. A good starting query:Common mistakes to avoid
Over-engineering the prompt
Over-engineering the prompt
Longer isn’t better. Every extra sentence is another chance to introduce a contradiction or an edge case the model over-indexes on. Start short, add only what a real failure justifies.
Telling Magic AI what NOT to do, over and over
Telling Magic AI what NOT to do, over and over
A prompt full of “never” and “do not” gives Magic AI a list of banned behaviors but no picture of the correct behavior. Rephrase as positive instructions: “Use first names” instead of “Never use titles like Mr. or Ms.”.
Contradicting your own examples
Contradicting your own examples
If the instruction says “use short sentences” but your example uses 40-word sentences, Magic AI will follow the example. Examples always beat instructions when they disagree — so review them together.
Assuming the model will 'just figure out' the context
Assuming the model will 'just figure out' the context
It won’t. Explicitly state who the audience is, what tools you use, what “urgent” means in your world, and what output format you need. Ambiguity in the prompt shows up as inconsistency in the output.
Referencing variable names in the prompt text
Referencing variable names in the prompt text
Write in natural language, not
{{variable_name}}. Variables are substituted with their real values before Magic AI ever sees the prompt — for the full explanation, see Using variables in Magic AI and Magic Agents.Never testing on real tickets
Never testing on real tickets
A prompt that works on invented examples often falls apart on the messy, half-punctuated, forwarded-three-times tickets you actually get. Always validate on real data before rolling out.
Where to go next
- Best practices for writing Magic AI prompts — the fundamentals, with worked examples for prioritization, categorization, recaps, and triage.
- Using variables in Magic AI and Magic Agents — how runtime substitution works and why you write prompts in natural language, not with variable names.
- Magic Title rule creation guide — applying these techniques to ticket title generation.
- Getting started with Triage Agent — applying these techniques to conversational triage.