Let’s face it: very few companies need to build an AI model from scratch. But many also want more control than they get from off-the-shelf APIs.
Enter the hybrid approach a smart middle ground that gives you power, flexibility, and efficiency without the full burden of DIY AI.
What Does a Hybrid Strategy Look Like?
A hybrid approach means leveraging large, pre-trained models and adapting them to your specific context, data, and goals. This might include:
1. Fine-Tuning or Prompt Engineering
- Use a general model like GPT-4 and customise its behaviour with training on your proprietary data.
- Add custom prompts, examples, and memory to guide behaviour.
2. Embedding + Retrieval-Augmented Generation (RAG)
- Use vector embeddings to connect external datasets or documents to a pre-trained model.
- The model can "reason" over private knowledge without retraining.
3. On-Prem Hosting of Distilled Models
- Use a distilled or quantised version of a large model on your own infrastructure for low-latency inference.
- Balances performance, privacy, and cost.
4. Chaining and Composition
- Combine multiple pre-built models into agents or workflows tailored to your specific business processes.
- Example: Use one model for classification, another for generation, and a third for summarisation.
Benefits of a Hybrid Approach
- Lower Cost: No need to fund full model training.
- Faster Time to Value: Rapid prototyping and iteration.
- Greater Control: Host models yourself or use private data securely.
- Easier Compliance: Keep sensitive data on-prem while using powerful cloud tools.
Real-World Examples
- A consultancy uses OpenAI’s API for client-facing reports but hosts a smaller, distilled model locally for internal operations.
- A medical provider combines open-source LLMs with embedded medical literature for on-demand clinical decision support.
- A supply chain firm fine-tunes a general model to automate highly specific vendor interactions.
What This Means for You
The future isn’t binary. You don’t have to choose between building your own model or being locked into someone else’s. Hybrid AI strategies let you experiment, scale, and innovate without unnecessary risk.
In the final post of this series, we’ll outline the critical questions every organisation should ask before deciding which path to take.
