Découvrez les secrets de SearchGPT: Leçons de construction de mon propre moteur de recherche et astuces d'ingénierie de prompt

Table of Contents

1. Introduction
2. Observation 1: Too Many Objectives at Once
3. Observation 2: Knowledge Cut-off and Supplemental Information
4. Observation 3: Leveraging APIs and Other Built-in Knowledge
5. Observation 4: 4000 Tokens Limit
6. Observation 5: Appify vs All GPT
7. Observation 6: Overfitting Prompts
8. Observation 7: Test, Test, and Iterate
9. Conclusion

Introduction

In this article, we will explore the observations made while building a search engine with Chat GPT. The focus will be on the lessons learned and best practices for building an effective and efficient search engine. We will Delve into topics such as objective management, knowledge cut-off, leveraging APIs, token limitations, appification, overfitting prompts, and the importance of testing and iterating. So, let's dive in and gain valuable insights into building a successful search engine.

Observation 1: Too Many Objectives at Once

One crucial lesson learned during the process of building a search engine is the importance of managing objectives effectively. Often, we tend to overload Chat GPT with multiple objectives simultaneously. While this may work in some cases, it generally leads to subpar results. To achieve optimal performance, it is recommended to focus on one objective at a time. By instructing Chat GPT to handle each objective separately, we can ensure that it performs each task to the best of its abilities. This approach prevents confusion and maintains Clarity in prompt engineering. So, remember to keep your objectives concise and tailored to specific tasks.

Observation 2: Knowledge Cut-off and Supplemental Information

One of the challenges encountered while building a search engine with Chat GPT is the knowledge cut-off. Chat GPT has a cut-off point where its training data ends, resulting in inaccurate or outdated information. To overcome this limitation, it becomes essential to provide supplemental information manually. Whether it's through search APIs or curated lists of the latest information, it is crucial to feed Chat GPT with up-to-date data. By sourcing and incorporating the most recent information, we can enhance the accuracy and relevance of search results, ensuring that users receive the most Current and accurate information possible.

Observation 3: Leveraging APIs and Other Built-in Knowledge

Understanding how to make the most of APIs and built-in knowledge is another valuable lesson in search engine development. Chat GPT is trained on a plethora of internet data and possesses knowledge about various APIs and their functionalities. Utilizing this built-in knowledge allows for more efficient and effective interactions. Rather than constructing complicated API calls and parsing JSON responses, instructing Chat GPT to construct structured data and accessing APIs directly can simplify the development process. Furthermore, this approach promotes better accuracy and precision in search results, making the search experience more reliable for users.

Observation 4: 4000 Tokens Limit

The token limit of 4000 tokens is often underestimated when building a search engine. While it may seem like a significant number, it can quickly be exhausted when dealing with extensive product information, descriptions, pricing details, and reviews. To tackle this issue, it is crucial to employ two strategies: summarization and selective data transmission. Summarization involves using non-GPT models or GPT itself to summarize information, reducing the number of tokens required. Selective data transmission, on the other HAND, involves passing only the necessary data to Chat GPT. By transmitting minimal information, unnecessary token usage is avoided, allowing for more efficient and effective interactions within the token limit.

Observation 5: Appify vs All GPT

An essential aspect of developing a search engine is the distinction between utilizing GPT for all tasks and appification. Instead of relying solely on Chat GPT for every operation, it is beneficial to delegate certain tasks to separate applications or platforms. By appifying the search process, we can alleviate the burden on Chat GPT and leverage specialized tools or apps for specific functionalities. This approach not only improves the overall performance but also provides a more seamless and user-friendly experience. By using Chat GPT for its specific strengths, such as natural language conversation, summarization, and query generation, and offloading other functions to dedicated apps, we can optimize the search engine's efficiency and effectiveness.

Observation 6: Overfitting Prompts

One common pitfall in prompt engineering is overfitting. Overfitting occurs when prompts become too specific, addressing only certain situations and failing to accommodate general queries effectively. Overfitting prompts can lead to reduced accuracy and precision, as Chat GPT becomes too reliant on explicitly Mentioned cases. To avoid overfitting, it is crucial to strike a balance between specificity and generality. By focusing on general prompts and creating separate prompts for specific cases, we can ensure that Chat GPT performs optimally across a range of queries and scenarios. Additionally, minimizing the use of "not" and "always" modifiers in prompts can help prevent overfitting and improve the prompt's flexibility and versatility.

Observation 7: Test, Test, and Iterate

Testing and iterating are essential aspects of search engine development. While personal testing provides a valuable starting point, it is crucial to involve real users in the testing process. Real users bring fresh perspectives and diverse scenarios, uncovering gaps, flaws, and areas for improvement. Hearing directly from users enables prompt refinement and optimization to cater to a wider range of queries and user intentions. Striking a balance between prompt customization and general applicability is key. Regular testing, combined with user feedback and iteration, ensures continuous improvement and a more refined search engine experience.

Conclusion

Building a search engine with Chat GPT involves various challenges and considerations. Through these observations, we have gained insights into effective prompt engineering, leveraging APIs and built-in knowledge, managing token limits, appification for enhanced performance, preventing overfitting, and the importance of testing and iterating. By incorporating these lessons into our search engine development process, we can Create more accurate, reliable, and user-friendly search engines that meet the needs and expectations of users. Remember to always optimize prompts, leverage available tools and knowledge, and iterate Based on user feedback to continually enhance the search experience.