AWS Certified Cloud Practitioner

Explanation

Setting temperature to 0 is the key strategy for achieving deterministic outputs from an LLM. Here's why:

Temperature Parameter

• Temperature controls randomness in LLM outputs

• Temperature = 0 means the model will always choose the most probable next token

• This eliminates randomness and ensures consistent outputs across multiple runs

Strict Output Format Specification

• Format constraints guide the LLM to produce structured outputs

• Schema specifications ensure the output follows a predictable pattern

• Validation rules can be applied to check output consistency

Why Other Options Are Incorrect:

A. Self-consistency decoding - This involves sampling multiple outputs and selecting the most frequent one, which doesn't guarantee deterministic results on a single run.

B. Few-shot prompting - While providing examples improves quality, it doesn't eliminate randomness in the output generation process.

D. Chain-of-thought prompting - This helps with reasoning but doesn't control the randomness of the output.

Practical Application for Database Schema Migration:

1. Set temperature = 0 to eliminate randomness

2. Define strict JSON/XML schema for output format

3. Include validation rules in the prompt

4. Use deterministic seed if available

5. Implement post-generation validation to ensure consistency

This approach is crucial for database migrations where consistency and reliability are paramount, as inconsistent scripts could lead to data corruption or deployment failures.