# Fine-tuning

Fine-tuning involves taking a pre-trained AI model, such as a large language model (LLM), and [training it further](https://www.ibm.com/think/topics/fine-tuning) on a smaller, specialized dataset so it performs better on a specific task. Fine-tuning teaches an existing model new patterns or behaviors that matter for a particular business or workflow rather than building a model from scratch.

Fine-tuning is also closely related to concepts like [model drift](/content/glossary/what-is-model-drift/index.html) and [prompt engineering](/content/glossary/what-is-prompt-engineering/index.html), both of which influence how consistently a model performs after deployment.

## How fine-tuning works

Fine-tuning begins with an already-trained model that has learned from billions of text samples and can understand natural language. Instead of redoing all that training (which can cost millions of dollars), a team:

**1. Collects a domain-specific dataset**  
Examples: customer service transcripts, brand voice guidelines, product descriptions, troubleshooting manuals, etc.

**2. Retrains the model on that smaller dataset**  
The model adjusts internal parameters so its responses reflect the tone, accuracy, terminology, and logic of the new information.

**3. Evaluates performance**  
Teams test the fine-tuned model to ensure it actually performs better at the specialized task.

**4. Deploys the fine-tuned version in production**  
It then powers chatbots, voicebots, agent-assist tools, workflow automation, or knowledge systems.

Fine-tuning is often cheaper and faster than building custom models, but it requires clean, high-quality data. Otherwise, the model will learn the wrong behaviors. Many companies also use fine-tuning alongside advanced retrieval methods, such as retrieval-augmented generation (RAG), to [further improve accuracy](https://www.intel.com/content/www/us/en/goal/how-to-implement-rag.html?cid=sem&source=sa360&campid=2025_ao_amr_us_comm_eai_eahqr_rep_awa_cons_txt_gen_exact_goog_is_intel_hq-eg-entai-obs_fc25023&ad_group=Gen_RAG-General-eai_b2b1-bp_Exact&intel_term=retrieval+augmented+generation&sa360id=2172439380435&gclsrc=aw.ds&gad_source=1&gad_campaignid=22195908923&gbraid=0AAAAA9YeOQQN_3o_4d_nP1h4XT5wWyKNA&gclid=Cj0KCQiArt_JBhCTARIsADQZaymLPO6P2PKdR_Qev49JFaT8ZyCnDAhs05LWwteCkGMuQBlVvqbLO6MaAtiuEALw_wcB).

## Fine-tuning improves precision in AI-based customer service

Customer service requires precision and brand consistency, which general-purpose AI models can’t guarantee out of the box. Fine-tuning solves core problems such as:

- **Brand voice alignment**—A fine-tuned model learns to speak exactly like the company—friendly, formal, concise, technical, or empathetic.
- **Reduced hallucinations**—Training on verified internal content reduces the risk of the AI “making up” answers.
- **Smarter automated interactions**—Bots handle more Tier-1 and Tier-2 issues because they're trained specifically on your policies, processes, and product details.
- **Better agent-assist tools**—Fine-tuning improves accuracy in summarizing tickets, suggesting resolutions, and retrieving knowledge.
- **Improved metrics (ART, FCR, AHT)**—Fine-tuned models can significantly lower [average resolution time (ART)](/content/glossary/what-is-average-resolution-time/index.html), improve [first-contact resolution (FCR)](/content/glossary/what-is-first-contact-resolution-fcr/index.html), and reduce agent workloads.

## Considerations for fine-tuning

Fine-tuning only works as well as the data you give it, which is why high-quality, up-to-date training material is essential. If the dataset contains outdated or inconsistent information, the model will learn those flaws, and performance may drift over time. As a result, fine-tuned models require regular updates as policies and processes evolve.

It’s also important to account for the cost and operational effort involved. While fine-tuning is far more affordable than training a model from scratch, it still requires thoughtful dataset preparation, testing, and monitoring to ensure the investment pays off.

Teams must also consider security and compliance, since fine-tuning often relies on internal documents or customer interactions. Sensitive information must be handled carefully and excluded from training data where appropriate.

Finally, a fine-tuned model should be monitored for inference time and latency, especially in real-time customer service environments. Even a highly accurate model can hinder the user experience if responses are slow or inconsistent at scale.