The standard prompt template
Every block prompt follows the same five-part scaffold.The ten rules that matter most
These come from VirtualBrain’s own deployment experience across customer Apps, not from published benchmarks.1
Open with a specific role
“You are a senior M&A analyst specialized in due diligence” consistently outperforms “Please help me with…”. The role sets vocabulary, depth, and judgment.
2
One objective per block
If you write “and then…” in a prompt, split the block. Two objectives in one prompt degrade both.
3
Instructions before data
Put Tasks above large variable injections: the Mainframe retains instructions better when it reads them first.
4
Numbered tasks, verb-first
“1. Extract X. 2. Classify each item. 3. Score against the rubric.” Enumerated flows are followed far more reliably than prose.
5
Keep prompts short
Target under 200 words, hard max 400. A 200-word prompt is followed more precisely than a 600-word one: long context is for data, not instructions.
6
Positive framing only
“Use only the terminology defined in the glossary” beats “don’t use informal language”. Tell the Mainframe what to do; negative instructions bloat prompts and underperform.
7
Say it once
Drop “be sure to…”, “remember that…”, “double-check…”. One clear instruction beats three nervous repetitions.
8
Embed domain terms verbatim
Scoring scales, business rules, thresholds, and frameworks go into the prompt exactly as the business defines them. Paraphrasing domain language creates drift.
9
Use the Mainframe's judgment for evaluation
“Apply your judgment to resolve conflicts between criterion A and B; briefly explain the trade-off” beats a brittle hard-coded decision tree.
10
Quote variables in tasks
‘Using the context in “context_notes”, prioritize…’ the explicit reference connects the instruction to the data.
A complete example prompt
What is not here: the candidate database (a Knowledge Source) is attached at block level and never listed under Inputs. The prompt instead tells the retrieval what to look for.
Build in quality-control checks
For high-stakes or calculation-heavy work, do not trust a single pass. Three techniques catch the most common failures before the user ever sees them.- Add validation instructions inside the prompt. Ask the block to verify its own work before producing the output, for example “recompute every total and confirm the columns add up”, “flag any two data points that contradict each other”, or “if a figure is missing or implausible, mark it rather than guessing”. Each takes one line and catches the most common failures.
- Use a dedicated quality-control block. Add a Hidden block whose only objective is to audit the previous block’s output: reconcile figures, surface inconsistencies, confirm every required section is present, and pass a short note plus any corrections downstream. Keep checking separate from producing: one block does the work, the next checks it.
- Make the check show its reconciliation. When numbers matter, have the check state what it verified (“totals match source: yes; dates consistent: yes; 1 outlier flagged”) so a reviewer can trust the output at a glance.
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Controlling the Output Format
Engineer the same recognizable layout on every run.