Topic 25: The Keys to Prompt Optimization

Practical Insights for Large Language Models

~ This is a part of our AI 101 series ~ Author of this issue: Isabel González Editor: Ksenia Se

Optimizing prompts is essential to improving the performance of large language models (LLMs). In this post, we will explore some of the keys to prompt optimization, drawing on recent research and practical techniques. Whether you’re looking to enhance the clarity of a query, break down complex questions, or maximize the relevance of retrieved information, these strategies will help you refine your approach and achieve better outcomes.

Everyone shall know them! Let’s go.

In today’s episode, we will cover:

• The Four Pillars of Query Optimization

1. Expansion
2. Decomposition
3. Disambiguation
4. Abstraction

• Combining strategies
• Conclusion
• Bonus: Resources to dive deeper

The Four Pillars of Query Optimization

Query optimization can be broken down into four primary strategies, each suited to different scenarios: Expansion, Decomposition, Disambiguation, and Abstraction. Let’s overview each of them with some relevany examples:

Expansion

One of the foundational techniques in prompt optimization is expansion, which involves enriching the original query with additional relevant information. Expansion is particularly useful for addressing gaps in context, uncovering hidden connections, or resolving ambiguities in the initial prompt.?

One specific application of query expansion is in retrieval-augmented generation (RAG) systems. In RAG, Large Language Models (LLMs) are used to generate text, but they often need to access external knowledge sources to provide accurate and comprehensive responses. Query expansion helps improve the retrieval of relevant documents from these knowledge sources, leading to better-informed LLM outputs.

Expansion can be categorized into two main types: internal expansion and external expansion.

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