Here’s something most people don’t know: there’s one word that makes AI give you short, straight answers.
You ask what a REST API is and you get three paragraphs about historical context, SOAP comparisons, security best practices, and a wrap-up that repeats everything. All you wanted was a definition.
One word cuts through all that: succinct.
Try it yourself with any LLM (Claude, ChatGPT, DeepSeek):
Without it: 800+ tokens.
With it: 80-90 tokens.
Same core info. About 9x fewer tokens. Works the same across all models.
This scales fast in production
Say your app makes 10,000 requests a day with 800-token responses. That’s 8 million tokens daily. Switch to succinct responses and you’re under 1 million.
Here’s why models default to long-winded: they assume “helpful” means “comprehensive.” You get context, examples, caveats, edge cases, plus a closing paragraph that rehashes everything. You don’t always need all that.
When you’re hitting APIs, every extra token costs real money and adds latency. An 800-token response takes noticeably longer to generate than a 90-token one.
Why this works (the technical bit)
When you add “succinct” to a prompt, you’re shifting which tokens the model thinks should come next.
LLMs predict the next token based on what came before. If the model starts with “Let me provide a comprehensive overview…”, it’s locked into a long response. That word “comprehensive” makes “detailed explanation,” “multiple examples,” and “various approaches” more likely to follow.
“Succinct” breaks that pattern right from the start. It favors tokens that wrap up ideas quickly instead of expanding them.
What this means for your architecture
If you’re building with LLMs, this changes a few things:
Context window efficiency: In a 10-turn conversation, if each response runs 800 tokens instead of 100, you’re filling up the context window 8x faster. With succinct prompts, conversations last longer without needing summarization.
Perceived latency: With streaming, users see the first tokens almost instantly. A 90-token response feels instant compared to 800.
Batch processing: Processing 1000 documents with 800-token outputs vs 90-token outputs? That’s the difference between 2 hours and 15 minutes. The bottleneck isn’t just cost—it’s how many requests fit in your rate limit.
The catch nobody mentions
Large models sometimes need tokens to think.
Ask GPT-4 to solve a complex math problem and those 500 tokens of step-by-step work aren’t fluff—they’re the model thinking out loud. Chain of Thought works because the model uses tokens to build its reasoning.
“Succinct” compresses output, but compress too hard on complex problems and the model skips steps and fails. This is an architecture decision, not just a prompting trick.
When to use it (and when not to)
Use it when:
- You need fast responses in production
- You’re hitting the API and paying per token
- You’re generating code you’ll review yourself
- You’re doing batch requests and speed matters
- The model tends to ramble by default
Skip it when:
- You’re learning something new (you need the examples)
- You’re debugging something complex (you want the full reasoning)
- You’re generating docs (comprehensive is the point)
- You need to explain it to someone else (context helps)
Try this
Grab your 3 most-used prompts. Run each one twice: normal, then with “Be succinct” tacked on.
Ask yourself: does the short version give me everything I need for the next step?
If yes, you just found a prompt you were over-optimizing. Most people find 2 out of 3 could be succinct.
Share what you discover. Tag @promptlucido with your findings.
This is the first article in the “Tricks” series at Beyond Vibe Coding. AI development for people who prefer to think.