Practical usage guide
What this tool does
Prompt Template Renderer combines reusable instruction text with a JSON object of variables and previews the exact prompt produced by deterministic placeholder substitution. Visible placeholders such as {{task}}, {{context}}, and {{user.name}} make repeated agent prompts easier to review and reproduce across tests.
Rendering is not a model call and does not judge prompt quality. The final text may still contain contradictory instructions, excessive context, missing delimiters, or prompt injection copied from an untrusted value. Review the boundary between fixed instructions and inserted content before sending it to any model.
Quick start
Write explicit placeholders
Put variable paths inside double braces and surround inserted documents or user content with clear labels and delimiters.
Provide a JSON variable object
Match keys exactly, use nested objects for dot paths, and use strings when whitespace or formatting must be preserved.
Inspect the final rendered prompt
Check unresolved placeholders, repeated context, spacing, role labels, and separation between trusted instructions and untrusted data.
Common use cases
Preview an agent prompt
Assemble task instructions, tool guidance, retrieved context, and output requirements before integrating a template into a workflow.
Produce repeatable test variants
Keep the instruction structure fixed while swapping datasets, user questions, personas, or expected formats for evaluation cases.
Review context assembly
Detect duplicated passages, missing delimiters, accidental variable names, and ordering problems before prompts reach a model or API log.
Limitations and important notes
The template syntax is intentionally small
The MVP replaces placeholders and nested dot paths; it does not provide loops, conditionals, filters, includes, expressions, or arbitrary code execution.
Substitution does not neutralize untrusted text
Inserted values may contain instructions that influence a model. Use delimiters, policy checks, allowlists, and runtime defenses appropriate to the application.
Rendering cannot predict model behavior
The tool does not calculate exact provider token use, test safety policies, guarantee structured output, or show how a particular model will respond.