Gen AI and LLMs (Large Language Models) continue to be a hot topic, and at their current pace of development, they're not likely to be going anywhere anytime soon. LLM-powered APIs such as OpenAI's GPT-4 are making it easier than ever before to leverage these models to turbocharge existing software applications or even build entirely new products. But how can you go about building Gen AI into your products? In this article, we'll break it down into 5 simple steps. Let's get started!
1. Engineering your prompts ✏️
It all starts with engineering your prompt. This involves a few upfront decisions such as picking the model that's appropriate for your use case, writing a prompt that succinctly summarizes the task and relevant context, configuring any prompt parameters available to you, and finally deciding whether to further refine the model you're using through fine-tuning or parameter-efficient-tuning.
1.1 Finding the perfect model ❤️
Unfortunately, this isn't a simple story of Prompt Engineer meets LLM and they lived happily ever after. Rather, navigating the Gen AI marketplace can be incredibly daunting. When looking at sites like HuggingFace 🤗 (a site that allows access and evaluates different Gen AI models), it's easy to become overwhelmed. But let's help you through that feeling of choice paralysis by going through a few of the key factors to consider when evaluating your options:
- Modality – What type of content do you want to generate? Images, text, audio, video, or even a combination of multiple? Depending on your circumstances, you might even need to consider using multiple models. Different models produce different content types, so being clear on your use case and objectives is key.
- Cost – What's the charge per API call? How many API calls will you have to make to deliver value to your users? What's the length of the inputs and outputs? Will you be charging your users, and if so, how will this cover your costs? All of these questions can help guide you towards an appropriate model.
- Response time – How important is it to get an immediate response to your users? Does speed beat quality or is it the other way around? This trade-off is worth considering when picking a model, even from the same provider. For example, ChatGPT-3.5-turbo is widely accepted to provide lower quality responses compared to its powerful younger sibling, ChatGPT-4. However, it easily has it beat on speed.
- Context limit – How long is the average message you'll have to send and receive from the API? Different models have different context limits, and this might guide you between choosing Anthropic's Claude 2 with its context limit of ~100k tokens versus GPT-4's default context limit of ~4k.
- Quality of response – There are a few different metrics that are widely used to compare Gen AI models. HuggingFace 🤗 has a published list of the top performers. Different models are better at different task types. Ultimately, it might be best to try out a few different models for yourself and understand what works best for you.
1.2 Writing your prompt
Once you've firmed up your base decisions around which model is right for you, you can delve into the world of prompt design. Best practices around how to do this are constantly evolving, but generally, it's accepted that a well-crafted prompt will cover the following:
- Context – Provide relevant context to the GenAI that will be helpful in ensuring a high-quality output. E.g., "You are an assistant that helps people plan trips."
- Instruct – Outline clear instructions that succinctly detail what is expected. E.g., "Plan a trip to Paris for a couple for 2 days."
- Examples – Provide examples of the expected response. This is also known as single-shot or even multi-shot prompting depending on how many examples are provided.
- Response format (if relevant) – It can also be useful to state how you expect to get a response. This could vary from being more vague and giving the LLM much greater flexibility to being highly prescriptive. E.g., "Provide a response as valid JSON with the following key-value pairs."
- Dealing with uncertainty – If the LLM is unsure in its response, it's often effective to give it permission to say so. This helps you minimize hallucinations (LLMs providing factually incorrect information) and is often preferable to it confidently providing an incorrect response.
2. Test, test, and test again 🧑💻
Testing is crucial when integrating Gen AI into your applications. Here's how to ensure your AI behaves as expected:
2.1 Dive into AI's unpredictability
Prepare for GenAI to sometimes offer different responses to identical prompts. This inherent non-determinism complicates testing scenarios, as the AI might produce varying results even under the same conditions. To manage this, it is important to develop robust testing methodologies that account for these variations, ensuring that the AI's performance remains consistent and reliable over time.
2.2 Root tests in reality
Construct canonical datasets and test cases that mirror genuine user interactions and business contexts. Establishing and maintaining these datasets is essential, as they provide a realistic foundation for evaluating the AI's performance. By grounding your tests in real-world scenarios, you can better predict how the AI will behave in actual use cases, leading to more effective and practical deployments.
2.3 Key challenges
One of the primary challenges in testing GenAI is its inherent non-determinism, which makes it difficult to predict and evaluate the AI's responses. This unpredictability requires a thorough and dynamic approach to testing, incorporating various test cases and scenarios to ensure comprehensive coverage.
Another significant challenge is establishing and maintaining canonical datasets that accurately reflect real-world user interactions. This involves continuous effort to keep the datasets up-to-date and relevant, ensuring they remain a valid benchmark for testing the AI's performance. Automated test runs based on these datasets can help streamline the process, but they require careful setup and ongoing maintenance to be effective.
3. Deploy with confidence 🚀
Deploying your GenAI model into a production environment is a critical step that requires meticulous planning and monitoring to ensure seamless integration and optimal performance. Here’s how to deploy with confidence:
3.1 Roll out flawlessly
Transitioning your GenAI-powered model from a development environment to production involves several key steps:
- Stage deployment: Start by deploying the model in a staging environment that closely mimics your production setup. This allows you to identify and resolve potential issues without impacting your users.
- Gradual rollout: Implement a gradual rollout strategy to minimize risks. Begin with a small subset of users or a specific region, monitor the performance, and gradually expand the rollout as confidence in the model grows.
- A/B testing: Utilize A/B testing to compare the new AI-driven features with the existing ones. This helps in understanding the impact of the new model on user experience and business metrics.
3.2 Aim for zero regression
Ensuring that new updates do not degrade the performance of your application is crucial:
- Continuous monitoring: Set up automated monitoring systems to track key performance indicators (KPIs) and alert you to any anomalies or regressions in real-time.
- Rollback strategy: Have a robust rollback strategy in place to quickly revert to a previous stable version if the new deployment introduces issues. This minimizes downtime and user impact.
- Feedback loops: Implement feedback loops to continuously collect and analyze user feedback. This helps in identifying any unforeseen issues and improving the model iteratively.
3.3 Key challenges
Avoiding regressions is one of the primary challenges when introducing new features to your GenAI model. Each update must be thoroughly tested and validated to ensure it does not negatively impact existing functionalities. This requires a detailed and systematic approach to testing, which includes the creation of comprehensive test cases and the use of automated testing tools.
Designing a robust rollback strategy is also essential. This strategy must allow for quick and efficient reversion to a previous stable version of your model without data loss or significant downtime. A well-planned rollback process ensures that any issues introduced by new updates can be swiftly addressed, minimizing the impact on your users and maintaining the reliability of your application.
4. Monitor your Gen-AI responses 👀
Continuous monitoring of your GenAI responses ensures that your application maintains high standards of quality and reliability:
4.1 Keep a watchful eye on your AI
Implement systems and processes to regularly monitor and evaluate AI outputs:
- Automated logging: Set up automated logging to capture and store all AI interactions. This data is invaluable for identifying trends, anomalies, and areas for improvement.
- Real-time assessment: Develop tools to assess the quality of AI responses in real-time. This could include measuring response accuracy, relevance, and user satisfaction.
- User feedback integration: Encourage users to provide feedback on AI interactions. This feedback should be systematically collected and analyzed to guide improvements.
4.2 Key challenges
Designing an efficient monitoring system is crucial for maintaining the quality and reliability of your GenAI application. The system must be capable of handling large volumes of data and providing meaningful insights in real-time. This involves selecting the right tools and technologies, setting up robust data pipelines, and implementing effective monitoring and alerting mechanisms.
Ensuring consistent quality of AI outputs is another significant challenge. As the underlying models and user interactions evolve, it is essential to maintain high standards of accuracy and relevance. This requires continuous evaluation and refinement of the models, as well as regular human-in-the-loop feedback to catch and correct any discrepancies.
5. Experiment and iterate 🧪
Experimentation and iteration are key to refining and enhancing your GenAI applications:
5.1 Make use of user feedback
Collecting user feedback is crucial for understanding the strengths and weaknesses of your GenAI application. Here are some strategies to effectively harvest user feedback:
- Feedback channels: Provide multiple channels for users to share their feedback, such as in-app feedback forms, surveys, or dedicated user forums.
- Feedback analysis: Analyze user feedback to identify patterns, common issues, and areas for improvement. This can help prioritize future iterations and enhancements.
- Iterative development: Use the insights gained from user feedback to drive iterative development. Continuously refine and enhance your GenAI application based on user needs and preferences.
5.2 Embrace continuous improvement
Continuous improvement is essential to keep your GenAI application relevant and effective. Here are some practices to embrace:
- Stay updated: Keep up with the latest advancements in Gen AI technologies and models. Regularly evaluate and consider integrating new models or techniques that can enhance your application.
- Monitor industry trends: Stay informed about industry trends and emerging use cases for Gen AI. This can help you identify new opportunities and stay ahead of the competition.
- Collaborate and share: Engage with the Gen AI community, share your experiences, and learn from others. Collaboration can lead to valuable insights and foster innovation.
Conclusion
By following these 5 steps and embracing continuous improvement, you can harness the power of Gen-AI to revolutionize your production applications. Get ready to unlock new possibilities and deliver exceptional user experiences with Gen-AI!