🅰️ℹ️ 1️⃣0️⃣1️⃣ The Keys to Prompt Optimization
~ 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.
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In today’s episode, we will cover:
The Four Pillars of Query Optimization
- Expansion
- Decomposition
- Disambiguation
- 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.
Internal Expansion
Internal expansion leverages the knowledge already embedded within the LLM during its pre-training. By analyzing the original query and adding contextual cues, synonyms, or associated ideas, the model can reframe the query into a more robust version.
For example, given a prompt like "What are the implications of climate change?", an internal expansion might incorporate phrases such as "effects on ecosystems, global temperature rise, and economic impacts" to guide the model toward a more comprehensive response. Internal expansion is particularly effective for queries with low temporal sensitivity, where the required information is already part of the model's knowledge base.
External Expansion
In contrast, external expansion incorporates supplementary data from sources outside the model, such as the web or structured knowledge bases. This method is especially valuable for time-sensitive queries or topics that require the latest information.
For instance, a prompt like "Where will the 2024 Olympics be held?" might be expanded by retrieving relevant updates or official announcements to ensure an accurate answer. External expansion effectively bridges the gap between static pre-trained knowledge and dynamic, real-world information.
Decomposition
Decomposition simplifies the query structure, reducing the cognitive load on the model and improving the likelihood of generating accurate responses. By tackling each component of a query independently, this method ensures that the final output is comprehensive and coherent.
When a query involves multiple layers of reasoning or requires integrating diverse pieces of information, a single prompt may overwhelm the model, leading to vague or incomplete answers. Advantages? Improved clarity, focused context and scalability.
Here we have some examples:
Sequential Decomposition
For queries that follow a logical progression, breaking the task into steps is essential.
- Original Query: "Which sport did China win more medals in during the 2024 Olympics: table tennis or badminton?"
- Decomposed Queries:
- Q1: "How many medals did China win in table tennis at the 2024 Olympics?"
- Q2: "How many medals did China win in badminton at the 2024 Olympics?"
The answers to these sub-queries can then be synthesized to determine the sport with more medals.
Parallel Decomposition
For queries requiring multiple independent facts, the components can be addressed simultaneously.
- Original Query: "What are the environmental impacts and economic effects of deforestation?"
- Decomposed Queries:
- Q1: "What are the environmental impacts of deforestation?"
- Q2: "What are the economic effects of deforestation?"
Each sub-query explores a distinct facet of the problem, ensuring no critical detail is overlooked.
Disambiguation
Language is inherently nuanced, and even a slight ambiguity in a query can lead to vastly different interpretations. For example, the query "Who is the 2024 Olympic table tennis champion?" could refer to either the men’s or women’s category, making it essential to refine the question to eliminate uncertainty. Disambiguation helps identify and eliminate such ambiguities by breaking down or rephrasing the query to ensure clarity.
Some practical examples of disambiguation:
Clarifying ambiguities
- Original query: "Who is the 2024 Olympic table tennis champion?"
- Disambiguated queries:
- Q1: "Who won the men’s singles table tennis championship at the 2024 Olympics?"
- Q2: "Who won the women’s singles table tennis championship at the 2024 Olympics?" This refinement ensures the model retrieves targeted answers for both categories.
Multi-Turn dialogues
In conversational settings, disambiguation can involve rephrasing queries by incorporating context from prior exchanges.
- Context:
- User: "Who won the gold medal?"
- Model: "In which event?"
- User: "Table tennis."
- Disambiguated query: "Who won the gold medal in table tennis at the 2024 Olympics?"
Reframing broad queries
- Original query: "What are the effects of climate change?"
- Disambiguated queries:
- Q1: "What are the environmental effects of climate change?"
- Q2: "What are the economic effects of climate change?"
This structured approach enables the model to explore distinct aspects of a complex issue comprehensively.
Abstraction
Abstraction helps shift the focus from granular details to the overarching goals, allowing the model to synthesize information and reason more effectively. This method is especially valuable in scenarios where a detailed step-by-step breakdown (like decomposition) might complicate the process further.
Some practical examples:
Broadening the scope
- Original query: "How many times has China hosted the Olympic Games?"
- Abstracted query: "What is the history of Olympic hosting by China?"
By reframing the query, the model is encouraged to provide a broader perspective, offering context and additional insights that go beyond the immediate question.
Core concept identification
- Original query: "What are the key economic impacts of deforestation in Brazil over the last decade?"
- Abstracted query: "What are the economic impacts of deforestation?"
Removing temporal and geographic constraints allows the model to explore foundational knowledge before adding context-specific details, ensuring a more comprehensive response.
Combining strategies
You can combine abstraction with other strategies by starting with an abstract query to identify key concepts, using decomposition to break it into smaller sub-questions, and applying expansion to enrich it with relevant details or data.
Conclusion
Without any doubt, prompt optimization is an art and science that combines understanding model behavior with a structured approach to query refinement. By leveraging the principles of expansion, decomposition, disambiguation, and abstraction, developers can unlock the full potential of LLMs across diverse applications. By addressing specific challenges in query processing, these pillars contribute to improving the accuracy, completeness, and overall quality of search results.
As the field evolves, integrating these techniques with robust benchmarks and tools will be key to advancing the effectiveness of LLM-driven systems. For a deeper exploration of these strategies, we highly encourage reviewing the insightful research A Survey of Query Optimization in Large Language Models by Mingyang Song and Mao Zheng, which provides a comprehensive foundation on query optimization in LLMs and highlights its transformative potential.
Start experimenting with these techniques today, and take the first step toward crafting more effective and impactful prompts for your projects!
About the author: Isabel Gonzalez / AI engineer with a passion for researching and developing technology in the field of innovation. With a solid background in ML, natural language processing, and computer vision, I am dedicated to sharing knowledge and experiences that inspire others to discover the fascinating universe of AI.
Bonus: Resources to dive deeper
- Exploring the Best Practices of Query Expansion with Large Language Models
- Corpus-Steered Query Expansion with Large Language Models
- Query Expansion by Prompting Large Language Models
- Optimizing Query Generation for Enhanced Document Retrieval in RAG
- Robust Prompt Optimization for Large Language Models Against Distribution Shifts
- Multi-Aspect Reviewed-Item Retrieval via LLM Query Decomposition and Aspect Fusion
- Decomposed Prompting: A Modular Approach for Solving Complex Tasks
- Abstraction-of-Thought Makes Language Models Better Reasoners
- Promptagator: Few-shot Dense Retrieval From 8 Examples
Sources from Turing Post
- What is HtmlRAG, Multimodal RAG and Agentic RAG?
- 12 Types of Retrieval-Augmented Generation (RAG)
- 16 New Types of Retrieval-Augmented Generation (RAG)
- Topic 3: What is Graph RAG approach?
- Topic 9: What is Speculative RAG?
- Topic 12: What is HybridRAG?
- 7 Free Courses to Master RAG
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