Lessons from Applying Generative AI to Solve Customer Problems
In the evolving landscape of artificial intelligence, leveraging generative AI (GenAI) to solve customer problems has opened up a ton of possibilities. Over the course of processing approximately one billion tokens, I’ve garnered invaluable insights into optimizing large language models for document analysis. This reflection aims to share these learnings, providing a roadmap for others getting into this field.
Workflow and Pipeline Optimization
The backbone of efficient GenAI implementation lies in a well-structured processing workflow. My workflow entails: document upload, information extraction, preprocessing such as named entity recognition and generating embeddings for retrieval augmented generation (RAG), prompt composition, LLM processing, and response handling. It’s funny how OCR extraction is often still the bottleneck of this whole processing pipeline. However, LLMs provide a significant improvement by accounting for faulty or redacted extractions, this comes at the cost of image processing which is still quite costly.
Challenges in Prompt Engineering
One of the things I spent the most time on by far was prompt engineering. Debugging prompts is a painstaking task, demanding great attention to detail. The key to overcoming this lies in being precise and consistent in language use. Ambiguities can lead to unexpected results, making it crucial to maintain clarity and consistency. By iteratively refining prompts and rigorously testing them, I found that it’s possible to navigate these challenges effectively.
Enhancing Robustness with Chain-of-Thought Reasoning
Chain-of-thought reasoning has emerged as a powerful tool in enhancing the robustness of results. By guiding the LLM through a logical reasoning process before it provides an answer, the outputs become more coherent and reliable. This method mirrors human cognitive processes, allowing the model to deliberate on its responses, thereby improving accuracy. Implementing this technique has significantly bolstered the quality of insights derived from the AI.
Evaluating and Optimizing Results
A pivotal aspect of working with LLMs is recognizing their limits. There were instances when adding complexity led to a decline in performance, indicating that the model’s capabilities were maxed out. To mitigate this, I constantly revisited and refined my prompts for logical consistency. Early evaluation of results proved essential, as did defining a gold standard. By aligning the model’s outputs with this benchmark, I could systematically optimize performance.
Experimentation with Semantic Text Metrics
Evaluating the quality of outputs necessitates the use of semantic text metrics. Through experimentation, I found that metrics like Meteor and Rouge scores were more indicative of performance than BERT scores. These metrics helped me understand the nuances in the model’s outputs, providing a clearer picture of its strengths and areas for improvement. This experimentation fostered a deeper intuition about what each metric captured, guiding my optimization efforts.
Debugging and Intermediate Evaluation
Providing output for debugging at initial stages is crucial for identifying issues early. Additionally, evaluating intermediate steps in the chain-of-thought reasoning process allowed for more granular control over the results. This approach ensured that any deviations from expected outcomes were promptly addressed, maintaining the integrity of the workflow.
Version Control and Frameworks
Managing the evolution of prompts necessitates robust version control. Utilizing systems like Git to store and track changes proved invaluable. Despite experimenting with various IDEs, I found them less effective compared to traditional version control. Moreover, frameworks like LangChain offered a quick setup for retrieval-augmented generation (RAG), though they posed challenges when customization was needed. Exploring these tools provided insights into their utility and limitations, guiding my approach to GenAI integration.
Conclusion
Reflecting on my journey, the lessons learned have been instrumental in refining my approach to using GenAI for solving customer problems. From mastering prompt engineering to leveraging chain-of-thought reasoning and experimenting with semantic metrics, each experience has contributed to a deeper understanding of this powerful technology. As we continue to innovate, these insights will serve as a foundation for advancing the capabilities and applications of generative AI, paving the way for more sophisticated and reliable solutions.