In the dynamic field of AI, validating that a solution is production-ready is a critical step. This process revolves around rigorous evaluations that focus on operational efficiency, safety, reliability and alignment with business requirements and company policies.
Why conduct a production-ready evaluation?
Conducting a thorough evaluation enables a reality check on:
- Whether model responses fulfil the intended requirements
- Whether the chosen AI model outperforms its alternatives
- Whether the RAG solution is optimised for efficiency
- Whether the AI application as a whole meets the desired standards
- Whether the solutions adhere to company policies and guidelines
Evaluating key components of your AI solution
- Model Responses – The primary checkpoint is the quality of the model responses. They need to be relevant, coherent, accurate and non-toxic while abiding by the company policies and guidelines. Positive user feedback is also a good indicator of high-quality responses.
- AI model performance – A keen eye for evaluating the chosen model’s performance against other alternatives is required, with special focus on factors like cost efficiency, latency and consistency.
- RAG performance – For RAG solutions, it’s crucial to ensure that the retrieved knowledge is relevant, that the model adheres to the provided context and that the solution is optimised for cost. For example, retrieving too many chunks can significantly increase the cost and lower the quality of the model’s responses.
- Prompt effectiveness – The prompt is more likely to serve its intended purpose if it bears clear and specific instructions, complies with the company guidelines and policies, and prevents toxic responses.
- AI solution evaluation – Finally, the AI solution as a whole must generate consistently high-quality responses while ensuring a positive user experience, backed by encouraging user feedback and effective RAG use.
Key metrics
There are a lot of metrics that can be used to ensure a solution is production-ready. For us, the key ones are:
- Accuracy – Are the facts provided in the response correct and up to date?
- Relevance – Does the AI model’s response align with the prompt’s intended purpose and context?
- Coherence – Is the AI model’s response logical, well-structured and easy to understand?
- Toxicity – Are the responses harmful, offensive, inappropriate or otherwise undesirable?
- Alignment – Are responses aligned with company guidelines and policies?
Other operational metrics like latency and cost also contribute to the AI solution’s overall effectiveness.
User feedback is a valuable metric, both for individual responses and the holistic AI solution. It offers direct insights into users’ perception of the solution. Adopting a systematic method of collecting and analysing this feedback facilitates continuous improvement to AI solutions.
The importance of ongoing AI solution evaluation
One key aspect to note in the production-readiness journey of an AI solution is that the evaluation process doesn’t end at the point of releasing the solution to production. It must be an ongoing practice – especially when the solution is live and operational. This is because of the unpredictable nature of AI models, which are subject to fluctuations over time. These fluctuations could be associated with various factors, including adapting to new data or alterations in the built-in guardrails.
Regular monitoring and recurrent evaluations ensure the AI solution remains efficient – and continues to meet the desired standards after it’s been deployed. This iterative loop of feedback and refinement ensures that AI solutions continue to deliver optimal results, maintain their quality and meet the dynamic needs of the business while consistently minimising the risk of performance deterioration. Therefore, a proactive approach towards evaluation, both pre and post-launch, is fundamental to the successful operation of AI solutions.
Moving from POC to production
In the quest to ensure your AI solution is production-ready, a thorough evaluation is indispensable. It ensures the solutions align with company guidelines, enhancing relevance and reducing the likelihood of toxicity. As AI technology continues to see significant advancements, regular evaluation remains essential, paving the way for optimised, effective and safe AI applications that are ready for real-world deployment.
