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For a short letter with a fixed structure and consequential details, explicit templates can be a better fit than an LLM call. In one practitioner account, a generator for recommendation, resignation, and notice letters replaced model output with roughly 200 lines of conditional code. That choice made the letters predictable, but not deeply personal or capable of rewriting arbitrary prose.
What the letter generator does
The example, described by Thedolceway on DEV Community, takes structured fields and returns a plain-text letter. It supports three kinds—recommendation, resignation, and notice—and three tones: formal, warm, and brief. A single exported function accepts typed input; greeting and sign-off functions adapt their wording to the selected tone, while conditional paragraphs depend on the letter kind and details supplied.
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Validation is separate from rendering. It checks different required inputs for recommendation letters than for resignation and notice letters, making the requirements explicit rather than leaving a model to infer what is missing.
Why the author chose templates
The output has a small, fixed shape
Each result follows a known sequence: date block, greeting, a few paragraphs, sign-off, and name. The author’s view is that model generation would approximate a finite set of conditions that code can express directly. That is a design rationale, not a measured comparison of model and template performance.
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Invented details could matter
These documents can become part of employment records. The author wanted to avoid a system inventing a notice period or personal fact. In this implementation, concrete details come from the form or from prewritten sentences, rather than being generated on the model’s initiative.
Per-use costs affect the product
Removing the model call also removed its per-use cost for this feature, supporting the author’s decision to keep the tool available without signup or payment details. This is the author’s product rationale; the account does not quantify costs or establish that templates are cheaper in every implementation.
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What deterministic templates make easier—and what they do not
- Repeatability: Given the same input, the template produces the same output, which the author says makes snapshot testing straightforward. For example, the three letter kinds combined with three tones yield nine kind-and-tone combinations to assert in tests; that is an example-specific test plan, not a general benchmark.
- Immediate, offline rendering: The author says the generator can render without a model service call, avoiding that call’s retries and timeouts in this feature.
- Explicit requirements: Per-kind validation forces the product to define which inputs a letter needs.
- Generic prose: Templates cannot supply a personally specific accomplishment that gives a reference letter substance. The implementation handles that with a free-text
highlightfield inserted verbatim. - No arbitrary rewriting: A template generator assembles known text; it does not rewrite any prose a user gives it.
- Code changes for new kinds: Adding a letter type requires changing the implementation. The author suggests reconsidering the approach if the number of kinds grows substantially.
How to choose between a template and an LLM
Use the shape and risk of the task—not a blanket preference for one technique—as the decision point. Thedolceway’s account supports a practical heuristic: templates fit a small set of acceptable outputs with a stable structure, especially when an invented or missing detail could cause harm. Generation is more suitable when good outputs vary widely and are difficult to enumerate in advance.
| Decision factor | Templates are a stronger fit when… | Generation is a stronger fit when… |
|---|---|---|
| Acceptable output space | There are few known document kinds and a stable structure. | Many valid outputs are hard to list or encode. |
| Cost of incorrect details | Personal or employment facts must come from user input or approved text. | The task can tolerate variation and has suitable review or safeguards. |
| Personalization and rewriting | Known fields and a specific user-written passage are enough. | Users need open-ended revision, synthesis, or substantially different phrasing. |
| Runtime needs | Immediate rendering without a model-service dependency is valuable. | A service dependency is acceptable for the flexibility the feature needs. |
| Ongoing maintenance | Maintaining a small set of explicit rules is manageable. | The number of types, variations, or languages would make templates cumbersome. |
Those comparisons are a framework for evaluating a feature, not results from a controlled test. The source describes one implementation and does not report measurements across competing systems.
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What the account establishes
Thedolceway’s DEV Community article, “I removed the LLM call and replaced it with 200 lines of template code”, reports an implementation estimated by its author at about 200 lines. The available posting date is August 25, with no year explicitly stated. It is a practitioner example and design argument, not evidence that templates are universally faster, safer, or cheaper than LLMs.
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