Generative ai from poc to production: a complete 9-step guide to success
Posted: Wed Dec 04, 2024 6:55 am
In recent years, generative AI and large-scale language models (LLMs) have made remarkable advances, and many companies are fascinated by their potential. The emergence of innovative models such as ChatGPT and GPT-4 has great potential for streamlining business processes and creating new products and services.
However, moving from an attractive proof of concept (PoC) to a production environment that delivers real business value is more difficult than many companies anticipated. This article details a comprehensive framework for unlocking the full potential of AI and effectively moving from PoC to production.
Why are businesses struggling to adopt AI?
2023 has been the year for many companies to explore the potential of generative AI. There have been many opportunities to build LLM-based PoCs and demonstrate syria b2b leads them to executives and stakeholders. These demos were certainly impressive and showcased the potential power of AI.
But as we move into 2024, many companies are facing a harsh reality: translating the possibilities demonstrated in PoCs into real business value is much harder than expected.
The main reasons include:
Lack of standardized processes : There is no established development and evaluation process that can handle the diverse use cases that utilize LLM. Many elements are different from traditional machine learning projects, and existing methods cannot be applied as is.
Lack of proper tooling : There are not enough tools available to address the unique challenges of generative AI projects (e.g. prompt engineering, hallucination prevention), forcing many companies to address these challenges on an individual basis.
No automated pipeline : No automated pipeline has been established to efficiently move from PoC to production and continuously monitor and improve, resulting in slow iteration cycles for AI development and wasted resources.
Lack of understanding of AI characteristics : Generative AI outputs are probabilistic and cannot be expected to behave deterministically like traditional software. Organizations lack the knowledge and skills to understand this characteristic and respond appropriately.
Issues with data quality and quantity : There are various issues related to data, such as securing high-quality training data, using data with consideration for privacy, etc. In many cases, a model that worked with a limited data set in a PoC does not perform as expected in a production environment that handles a wider range of data.
A practical approach is needed to effectively address these challenges and integrate LLMs into systems and products. This article details nine key steps to overcome these challenges and ensure your generative AI project is successful.
Migrating to production: 9 key steps
There are nine key steps to successfully deploying an AI-driven solution in production. Let's take a closer look at each step, its importance and practical approaches.
1. Evals: Accurately Measure LLM Performance
Evals are a crucial component of any successful AI project. Accurately measuring the performance of your LLM allows you to understand the strengths and weaknesses of your model and pinpoint areas for improvement.
Key Takeaways:
Prepare diverse test cases: Prepare tests with various input patterns and levels of difficulty to evaluate the versatility of the model.
Combined quantitative and qualitative evaluation: In addition to numerical metrics, the quality and appropriateness of the output are also assessed by human experts.
Ongoing evaluation: Evaluate periodically as models are updated and the environment changes.
Example of Practice: Chatbots at financial institutions are evaluated from the perspective of the accuracy, appropriateness, and legal compliance of responses to customer inquiries. For example, experts regularly evaluate the bots to see if the explanations of investment risks are appropriate and if there are any problems with the handling of personal information.
2. RAG (Search Augmentation Generation): Effectively integrate the latest external knowledge
Retrieval-Augmented Generation (RAG) is a key technique for augmenting the knowledge of LLMs with up-to-date and accurate information, allowing the model to go beyond its known knowledge and generate responses that are always up-to-date.
Key Takeaways:
Identify trusted sources: Build a database of up-to-date industry-specific information and in-house expertise.
Implement efficient search algorithms: Develop a search system to quickly extract relevant information.
Proper integration of context: Optimize how search results can be effectively incorporated into LLM inputs.
Example: An e-commerce platform uses RAG to create chatbots that dynamically update product information and stock availability, so that answers such as "What is the last price of this item?" can be generated with up-to-date pricing information.
3. Fine-tuning: Improving performance for a specific task or domain
Fine-tuning is the process of taking a generic LLM and making it mission- or domain-specific, creating an AI model that reflects an organization's unique idiom and expertise.
Key Takeaways:
Prepare high-quality training data: Prepare reliable datasets that are relevant to the target task or domain.
Prevent overfitting: Set an appropriate amount of data and number of training epochs to maintain the model's generalization ability.
Continuous performance evaluation: Compare performance before and after fine-tuning to measure the improvement effect.
Practical example: A law firm has used LLM for legal document analysis and summarization tasks. By fine-tuning on case- and statute-specific datasets, the model has developed an accurate understanding and processing of legal-specific terminology and context.
4. Simplified data storage and processing: faster response times and reduced costs
Operating large-scale LLMs comes with challenges in terms of computational cost and latency. Caching and simplified processing are key techniques that mitigate these challenges and enable the building of efficient AI systems.
However, moving from an attractive proof of concept (PoC) to a production environment that delivers real business value is more difficult than many companies anticipated. This article details a comprehensive framework for unlocking the full potential of AI and effectively moving from PoC to production.
Why are businesses struggling to adopt AI?
2023 has been the year for many companies to explore the potential of generative AI. There have been many opportunities to build LLM-based PoCs and demonstrate syria b2b leads them to executives and stakeholders. These demos were certainly impressive and showcased the potential power of AI.
But as we move into 2024, many companies are facing a harsh reality: translating the possibilities demonstrated in PoCs into real business value is much harder than expected.
The main reasons include:
Lack of standardized processes : There is no established development and evaluation process that can handle the diverse use cases that utilize LLM. Many elements are different from traditional machine learning projects, and existing methods cannot be applied as is.
Lack of proper tooling : There are not enough tools available to address the unique challenges of generative AI projects (e.g. prompt engineering, hallucination prevention), forcing many companies to address these challenges on an individual basis.
No automated pipeline : No automated pipeline has been established to efficiently move from PoC to production and continuously monitor and improve, resulting in slow iteration cycles for AI development and wasted resources.
Lack of understanding of AI characteristics : Generative AI outputs are probabilistic and cannot be expected to behave deterministically like traditional software. Organizations lack the knowledge and skills to understand this characteristic and respond appropriately.
Issues with data quality and quantity : There are various issues related to data, such as securing high-quality training data, using data with consideration for privacy, etc. In many cases, a model that worked with a limited data set in a PoC does not perform as expected in a production environment that handles a wider range of data.
A practical approach is needed to effectively address these challenges and integrate LLMs into systems and products. This article details nine key steps to overcome these challenges and ensure your generative AI project is successful.
Migrating to production: 9 key steps
There are nine key steps to successfully deploying an AI-driven solution in production. Let's take a closer look at each step, its importance and practical approaches.
1. Evals: Accurately Measure LLM Performance
Evals are a crucial component of any successful AI project. Accurately measuring the performance of your LLM allows you to understand the strengths and weaknesses of your model and pinpoint areas for improvement.
Key Takeaways:
Prepare diverse test cases: Prepare tests with various input patterns and levels of difficulty to evaluate the versatility of the model.
Combined quantitative and qualitative evaluation: In addition to numerical metrics, the quality and appropriateness of the output are also assessed by human experts.
Ongoing evaluation: Evaluate periodically as models are updated and the environment changes.
Example of Practice: Chatbots at financial institutions are evaluated from the perspective of the accuracy, appropriateness, and legal compliance of responses to customer inquiries. For example, experts regularly evaluate the bots to see if the explanations of investment risks are appropriate and if there are any problems with the handling of personal information.
2. RAG (Search Augmentation Generation): Effectively integrate the latest external knowledge
Retrieval-Augmented Generation (RAG) is a key technique for augmenting the knowledge of LLMs with up-to-date and accurate information, allowing the model to go beyond its known knowledge and generate responses that are always up-to-date.
Key Takeaways:
Identify trusted sources: Build a database of up-to-date industry-specific information and in-house expertise.
Implement efficient search algorithms: Develop a search system to quickly extract relevant information.
Proper integration of context: Optimize how search results can be effectively incorporated into LLM inputs.
Example: An e-commerce platform uses RAG to create chatbots that dynamically update product information and stock availability, so that answers such as "What is the last price of this item?" can be generated with up-to-date pricing information.
3. Fine-tuning: Improving performance for a specific task or domain
Fine-tuning is the process of taking a generic LLM and making it mission- or domain-specific, creating an AI model that reflects an organization's unique idiom and expertise.
Key Takeaways:
Prepare high-quality training data: Prepare reliable datasets that are relevant to the target task or domain.
Prevent overfitting: Set an appropriate amount of data and number of training epochs to maintain the model's generalization ability.
Continuous performance evaluation: Compare performance before and after fine-tuning to measure the improvement effect.
Practical example: A law firm has used LLM for legal document analysis and summarization tasks. By fine-tuning on case- and statute-specific datasets, the model has developed an accurate understanding and processing of legal-specific terminology and context.
4. Simplified data storage and processing: faster response times and reduced costs
Operating large-scale LLMs comes with challenges in terms of computational cost and latency. Caching and simplified processing are key techniques that mitigate these challenges and enable the building of efficient AI systems.