AI isn’t vague. your instructions are.
most disappointing AI outputs aren’t an AI problem. they’re a communication problem. the model responded to exactly what you said — just not what you meant. closing that gap is what description is about.
description isn’t just writing better prompts. it’s building a collaborative environment where AI can actually do its best work.
three dimensions of description#
the AI fluency framework breaks description into three components. most people only think about one.
product description — define what you want
the output. format, length, audience, style, level of detail.
vague: “summarise this alert.” specific: “summarise this alert in 3 bullet points for a non-technical stakeholder. focus on what happened, what the risk is, and what action is recommended.”
same task. very different results.
for SOC work — be explicit about who the output is for. a summary for a CISO reads differently than one for a tier 1 analyst. AI doesn’t know your audience unless you tell it.
process description — guide how AI approaches the task
not just what you want, but how you want AI to get there. the methodology, the steps, the framework to follow.
vague: “analyse this log data.” specific: “analyse this log data by first identifying anomalous timestamps, then cross-referencing with the associated user account activity, then flagging anything that deviates from the baseline behaviour described below.”
process description is particularly powerful for complex, multi-step SOC tasks where the approach matters as much as the output.
performance description — define how AI should behave
the collaboration style. should AI be concise or detailed? challenging or supportive? should it ask clarifying questions or make assumptions and proceed?
this is the most overlooked dimension of description. most people never specify it — then wonder why the interaction feels off.
examples:
- “be concise. one paragraph maximum per point.”
- “challenge my assumptions. push back if something doesn’t hold up.”
- “ask me clarifying questions before starting.”
- “think step by step and show your reasoning.”
for SOC investigations — telling AI to challenge your assumptions is genuinely useful. it surfaces blind spots you might miss under time pressure.
six prompting techniques worth knowing#
these are practical, not theoretical. use them.
1. give context who you are, what you’re working on, why it matters. “i’m a SOC L2 analyst investigating a potential phishing incident affecting a finance team member” lands very differently than “help me with phishing.”
2. show examples demonstrate the output you want. paste a previous report you liked. show a format that worked. AI learns from examples faster than from descriptions alone.
3. specify constraints format, length, tone, what to include, what to exclude. be explicit. “under 150 words, bullet points, no jargon, include a recommended action” removes ambiguity entirely.
4. break complex tasks into steps chain-of-thought prompting. list the specific steps you want AI to follow. especially useful for multi-stage investigations or analysis tasks where order matters.
5. ask AI to think first explicitly instruct AI to work through its reasoning before giving a final answer. “think through this carefully before responding” consistently improves output quality on complex tasks.
6. define the role or tone assign a perspective. “respond as a senior threat intelligence analyst reviewing this for a junior colleague” shapes both expertise level and communication style in one instruction.
one technique worth calling out separately#
if you’re struggling to write a good prompt — ask AI to help you write it.
“i need to ask you about X but i’m not sure how to frame it. can you help me write a better prompt?” works surprisingly well and saves significant back and forth.
the iteration mindset#
first response is a draft. not a final answer.
description isn’t a one-shot exercise. it’s a conversation. if the output isn’t right — refine, redirect, give feedback. “this is good but too formal, make it more direct” is a valid next prompt.
the analysts who get the best results from AI aren’t the ones who write perfect prompts first time. they’re the ones who iterate quickly and know what good looks like.
next up — discernment. because description gets you better outputs, but discernment is what stops you from using bad ones.
which of the three description components — product, process, or performance — do you currently use least? that’s probably where your biggest gains are.
took ai help to clean up typos. my brain works faster than my fingers. xd
next up: AI Series #22 — “just because it sounds right doesn’t mean it is” AI Fluency Cheat Sheet back to series index
