Fine-Tune and RAG creates business value

Fine-Tuning: A cost-effective path to a specialised agent

Fine-tuning takes a pre-trained language model and adjusts it to your industry. Instead of relying solely on generic, large-scale models that can be expensive in production, you can create smaller, more targeted models.

• Cost Savings

  Fine-tuned smaller models can reduce operational costs by up to 50% while maintaining over 90% of the performance of large, generic models.

• Faster Performance

  Fine-tuned models often show quicker response times due to their reduced size and narrower focus.

By deploying models refined to your content (e.g., customer support transcripts, technical documentation, or marketing copy), you unlock more accurate and consistent on-brand interactions without paying for the overhead of massive, general-purpose models.

RAG: Your real-time company brain

Retrieval-augmented generation (RAG) integrates a retrieval engine that pulls relevant data on demand from a document repository or knowledge base. Instead of hoping your model “remembers” everything, it fetches the latest information at inference time.

• Accuracy Boost

  Tests by McKinsey & Company in implementing RAG improved customer query resolution by 35%, as models no longer relied on static knowledge.

• Adaptable to Change

  When policies, pricing, or product details shift, RAG ensures your AI always references the most recent data without needing a full model retrain.

Combining Fine-Tuning and RAG for Maximum Impact

A common strategy is to fine-tune a model for your recurring, domain-specific tasks (like the tone of voice or compliance requirements) and then augment it with RAG for real-time, dynamic updates. For instance:

1. Fine-Tune

   a smaller model to handle typical customer queries in a highly specialised manner.

2. Employ RAG

   to fetch up-to-date policy changes, user account data, or the latest product details when unusual or rapidly evolving questions arise.

You benefit from the tailored accuracy of a fine-tuned model and the adaptive, real-time responses of RAG.

RAG for Knowledge. Fine-tune for Behaviour.

Delivering Tangible Business Outcomes

• Higher Customer Engagement

  Personalised and precise responses foster trust and loyalty.

• Reduced Operational Costs

  Smaller, optimised models cut costs on cloud infrastructure and reduce latency.

• Easier Scalability

  As new data emerges, RAG can integrate updates seamlessly without repeatedly retraining the entire model.

If you’re seeking a balance between personalised, on-brand outputs and the flexibility to respond to fast-changing data, consider adopting fine-tuned models alongside a robust RAG system. Together, they can keep your AI offering fresh, accurate, and cost-effective in a world that’s constantly evolving.

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