Prompts Reliability

## Reliability

We have seen already how effective well-crafted prompts can be for various tasks using techniques like few-shot learning. As we think about building real-world applications on top of LLMs, it becomes crucial to think about the reliability of these language models. This guide focuses on demonstrating effective prompting techniques to improve the reliability of LLMs like GPT-3. Some topics of interest include generalizability, calibration, biases, social biases, and factuality to name a few.

**Note that this section is under heavy development.**

Topics:
- [Factuality](#factuality)
- [Biases](#biases)
- ...

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## Factuality
LLMs have a tendency to generate responses that sounds coherent and convincing but can sometimes be made up. Improving prompts can help improve the model to generate more accurate/factual responses and reduce the likelihood to generate inconsistent and made up responses. 

Some solutions might include:
- provide ground truth (e.g., related article paragraph or Wikipedia entry) as part of context to reduce the likelihood of the model producing made up text.
- configure the model to produce less diverse responses by decreasing the probability parameters and instructing it to admit (e.g., "I don't know") when it doesn't know the answer. 
- provide in the prompt a combination of examples of questions and responses that it might know about and not know about

Let's look at a simple example:

*Prompt:*