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For a short document with a fixed structure and costly errors, templates can be a better fit than an LLM call. In one practitioner’s letter-generator example, recommendation, resignation, and notice letters are assembled from typed form data and prewritten sentences rather than generated by a model. That is a case-specific design choice—not proof that templates are always superior.
What the letter generator does
The implementation described by Thedolceway takes a small set of fields and returns a plain-text letter. It supports three kinds—recommendation, resignation, and notice—and three tones: formal, warm, and brief. One exported function receives typed input; greeting and sign-off functions adjust wording for the selected tone, while conditional paragraphs depend on the letter kind and details supplied.
A separate validator checks that the required fields for the selected kind are present. Recommendation letters and resignation or notice letters do not require exactly the same inputs, so validation makes those differences explicit rather than leaving a model to infer missing details.
Why the author removed the model call
The acceptable output shape is narrow
The author describes each result as a date block, greeting, a few paragraphs, sign-off, and name. Because both the structure and the available conditions are limited, the author viewed generation as an expensive approximation of choices that could be enumerated directly.
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Employment details should not be invented
These letters are legal-adjacent and may enter employment records. The author’s concern was that a model might supply an incorrect notice period or personal fact. In this design, specific details must come from the form or from sentences written in advance; the implementation is intended to avoid fabricating missing facts, not to provide legal advice or guarantee that a letter is appropriate for every situation.
Removing per-use model charges suited the product
The author also wanted the feature to remain available without requiring signup or payment details. Avoiding a model call supported that product decision by eliminating a per-use model cost for this feature. The article reports the rationale, not a measured cost comparison.
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What deterministic templates make easier—and what they do not
- Repeatable output: With the same input, the code returns the same wording, which the author says makes snapshot testing straightforward. In this example, testing three kinds across three tones would mean nine combinations to check; that is an example-specific test plan, not a general benchmark.
- Offline, immediate rendering: The author says the letters can be rendered locally without waiting for a model service or handling its timeouts and retries.
- Explicit requirements: A validator makes required fields visible in the product logic and lets the interface identify missing information.
- Generic prose: A template cannot supply the personally specific accomplishment that gives a reference letter substance. The example retains a free-text
highlightfield and inserts its contents verbatim. - Limited rewriting and expansion: The templates do not rewrite arbitrary prose, and a new letter kind requires code. If kinds or variations grow substantially, maintaining the branches may become less attractive.
When to choose templates instead of an LLM
For a similar feature, decide based on the shape of the task rather than a general preference for or against AI. Templates are a strong candidate when acceptable outputs are few, their structure is stable, and fabricated or missing details could have meaningful consequences. Generation is more useful when good outputs are numerous and difficult to enumerate, or when users need open-ended rewriting and personalization.
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| Decision factor | Templates are a better fit when… | Generation is a better fit when… |
|---|---|---|
| Output space | There are a few well-defined document types and structures. | Good results vary widely and are hard to describe as branches. |
| Error cost | Invented dates, terms, or personal facts would be costly, and details can be required from users. | The task can tolerate generated variation and has a suitable way to review or correct it. |
| Personalization | Users can provide the specific facts in fields that are inserted into controlled prose. | Users need the system to transform arbitrary input or create varied prose from context. |
| Runtime needs | Immediate or offline rendering and independence from a model service matter. | A service dependency and model-call latency are acceptable for the added flexibility. |
| Ongoing maintenance | The supported types and tones are stable enough to maintain explicitly. | The space of variations is too large to maintain as templates, though generation still needs safeguards and review. |
Thedolceway estimates that this particular implementation took about two hundred lines of template code. That figure describes one implementation, not a universal threshold for when templates become worthwhile. The source does not report a comparative benchmark of development time, accuracy, or operating cost.
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How to apply the same approach safely
- Define the output before choosing a technique. List the document types, sections, tone options, and variables. If the structure and allowed variations are small, they can be represented directly in code.
- Separate user facts from fixed wording. Require values that affect the letter’s meaning, and keep variable sentences out of the output when their supporting information is absent. Do not let a default template imply a fact the user did not provide.
- Validate by document type. Check the required fields for each kind rather than applying one universal checklist. Show missing-field errors before producing a final document.
- Test combinations and edge cases. Snapshot tests can catch unintended wording changes across supported kinds and tones. Also check missing fields and any free-text insertion behavior; repeatable output alone does not establish that wording is suitable or legally correct.
- Reassess as the product grows. New document types, languages, and increasingly varied wording add maintenance work. If users need broad rewriting or the number of acceptable outputs becomes difficult to enumerate, the original trade-off may no longer hold.
The practical rule
The author’s heuristic is conditional: use templates when the output space is small, the shape is fixed, and the cost of being wrong is high; use generation when good outputs are numerous and hard to enumerate. In the letter example, templates control structure and keep factual details tied to user input, while a free-text highlight field leaves room for the user’s own evidence.
Read Thedolceway’s original account on DEV Community.
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