Transition from closed-source LLM APIs used for prototyping and MVPs, to owning fast, accurate & cost-effective fine-tuned LLMs tailored to your specific needs.
At AiTuning, our team curates precise training data specific to your AI agents use cases, and then continuously fine-tunes your own proprietary models to ensure they are not only more cost-effective but also superior in performance and adaptability.
Our collaboration begins with an in-depth analysis of your AI project, conducted hand-in-hand with your technical team. We meticulously review each prompt currently deployed which rely on costly, slow, and less accurate closed-source AI providers like OpenAI, Anthropic, and Gemini. This thorough examination allows us to identify specific prompts where performance enhancements and significant cost reductions can be achieved.
Our expertise is particularly valuable in fine-tuning AI agents that require complex, multi-step reasoning or function calls. Few-shot learning prompts are also a major opportunity for fine-tuning as we can reduce the input tokens required to get the same result.
By identifying these high ROI opportunities at the beginning, we ensure that the fine-tuning process will definitely enhance the efficiency and accuracy of your AI project before we ever get started.
To ensure we set a high standard of accountability and precision early on, we focus on creating custom evaluations for each of your prompts that shows potential for fine-tuning.
These bespoke evals provide concrete insights into the speed, accuracy, and cost of your current AI outputs. For instance, if your prompt involves processing unstructured text into a structured JSON format, we meticulously craft evals for each potential edge case associated with this prompt. As we know the expected input and output for each edge case, we can rigorously assess performance & cost.
These custom evals are crucial as they not only help in pinpointing areas where the model may falter but also provide a clear benchmark for post-tuning performance comparison. Now we can ensure that the fine-tuned models we deliver are not only specialised but also faster, cheaper & superior in handling real-world complexities and variations.
Models are only as good as the data they consume, which is another way of saying the value is not in the final model, but the dataset used to train it. This is why our data curation process is human driven and tailored to enhance the specific needs of your project.
When you have existing data, our team conducts a thorough review of each data point to ensure its relevance and quality. We then organise this data by identified edge cases.
In scenarios where you may not have the necessary data, AiTuning steps in to generate synthetic data for each identified edge case. Every piece of synthetic data is carefully reviewed by our team to ensure it meets the high standards required for effective training.
By combining high-quality, real, and synthetic data, we create a diverse and comprehensive training dataset that is crucial for developing highly performant AI models.
The cornerstone of our approach is the recognition that the dataset, rather than the model itself, is your most critical asset. Training AI models is relatively inexpensive, so we leverage the flexibility of multiple training runs.
Each fine-tuned model is trained on the same dataset and then tested against the bespoke evals we've developed. These evals are designed to measure how well each model handles the specific complexities and variations of your project.
We typically begin by training smaller models, ranging from 4B to 8B parameters, especially for tasks involving function calls. These smaller models are not only radically more cost-effective but also offer faster response times, making them ideal for real-time applications. However, the quest for maximum accuracy might lead us to consider larger models, potentially up to 70B+ parameters, depending on the complexity and demands of the task.
By running these evaluations, we can objectively assess which model performs best in terms of speed, accuracy, and cost-efficiency.
When a more advanced model is released that seems to outperform your current model, we simply use the existing datasets to train the new model for you at no extra cost. Through our evaluations, we assess whether the new model offers enhanced performance, and, If the newer model is superior, we simply swap it in.
Additionally, we continuously experiment with ways to streamline your AI interactions. This includes refining the prompts used to interact with your model to reduce the number of input tokens required. By rigorously testing new prompts against our evals, we aim to enhance the model's performance while reducing operational costs.
Our commitment to continuous improvement also extends to the real-world application of your AI. Whether deployed in shadow mode or fully operational, every interaction with your model is carefully logged. This data is invaluable as it uncovers previously unidentified edge cases and scenarios that arise in production, allowing us to continuously expand and refine your training dataset to include these new cases. This iterative process allows us to re-train and enhance your model, ensuring it gets better over time.
This dual approach of free model upgrades and dynamic, data-driven refinement ensures that your AI not only starts strong but also improves continuously, adapting to both technological advancements and evolving real-world demands. This optional add-on service is designed to provide you with a sustainable competitive advantage, making your AI investment future-proof.
