Coding Recommendations

Prioritizing Very High Precision in Code Generation and Debugging

When working with AI-assisted coding, precision is non-negotiable. Even minor hallucinations, syntactic errors, or incorrect logic can introduce bugs, security vulnerabilities, or maintenance debt. These recommendations are tailored for developers who require extremely high-fidelity outputs—not just plausible-looking code, but correct, production-ready, and logically sound implementations.

Use the selector below to identify the best model for a given hardware configuration:

RAM (GB)163264128

VRAM (GB)046812162432

Context Size: 16384 tokens

Recommended model:Qwen3 4B Instruct 2507 (F16) Use in LMStudio

Parameters: 4B | Quantization: F16 | Model Memory Impact: 8.0 GB

Context Overhead: 1.27GB (based on 16,384 token context)
Note: The size of context in GB is an estimate that shows the "worst case". It may be smaller in actual use, but is assumed to be higher for safely assessing upper bounds.

	Total	Context	Model	Leftover
RAM	16.00GB	0.00GB	3.00GB	5.00GB
VRAM	8.00GB	1.27GB	5.00GB	0.23GB

Prompting

To effectively prompt coding models in a chat-based interface, consider the following guidelines:

1. State your goal clearly. Provide context for your request. An authoritative tone often yields better results.
2. Use code blocks. Enclose any code, error messages, data structures, or formatting examples within markdown code blocks for clarity.
3. Provide examples. Include relevant code snippets or data samples to illustrate your request.

Below are some examples of well-structured prompts, followed by examples of what to avoid.

Effective Examples

Generating a Function

This prompt clearly defines the input data structure and the desired outcome.

Write a JavaScript function that generates a descriptive sentence about a person, given an input object with the following structure:

```ts
type Status = "dead" | "alive";

interface Person {
  name: string;
  age: number;
  status: Status;
}
```

The function should return a grammatically correct sentence.

Requesting Debugging Help

This prompt provides both the error and the code that produced it, creating a complete context for the model.

I have encountered this error:

```
Error: Cannot divide by zero
```

In the following JavaScript code:

```js
function divide(a, b) {
  if (b === 0) {
    throw new Error("Cannot divide by zero");
  }
  return a / b;
}
```

Can you help me refactor the error handling to be more user-friendly?

Poor Examples (What to Avoid)

The following prompts are less effective because they lack necessary context, formatting, and clarity, forcing the model to guess.

Vague Function Request

This prompt lacks structure and mixes the data description with the instruction.

make a js function. it takes a person with a name, age, and status (dead or alive) and it should make a sentence about them.

Why it's ineffective:

• No Code Formatting: The description of the Person object is not in a code block, making it harder to parse.
• Ambiguous: The request "make a sentence" is vague and leaves too much room for interpretation.
• Lacks Authority: The tone is casual and less direct, which can sometimes lead to less precise results.

Incomplete Debugging Request

This prompt mentions an error but provides no code, making it impossible to solve.

My code is broken. It gives me a "cannot divide by zero" error. How do I fix my code?

Why it's ineffective:

• No Code Context: Without the relevant code, the model cannot identify the source of the error.
• No Error Formatting: The error message is not placed in a code block, blending in with the rest of the text.
• Too Broad: "How do I fix my code?" is an unanswerable question without seeing the code in question.