The value of large language models (LLMs) is manifesting in a wide array of strategic applications. However, the success of LLM-powered applications often hinges on having a solid foundational knowledge database. This knowledge database becomes an even more powerful enabler for the applications when it is trained with proprietary enterprise data.
Imagine building a knowledge database with a generic foundational model from OpenAI or Meta. The output will be generic, inaccurate, or hallucinated in numerous instances. Enterprises often end up losing users (for customer-facing applications) or internal teams (for internal applications). This defeats the intended purpose.
In a different application, where a knowledge database was being used to power a chatbot and customer service teams, without fine-tuning, the customer service team:
- Did not experience productivity gains as the chatbot couldn’t provide meaningful responses
- Saw a higher number of dissatisfied customers with an increased risk of churn as a result of escalations
- Couldn’t use the database to find answers quickly
This is because foundational LLMs, while effective in some contexts, lack the precision, industry-specific knowledge, and standards required for specific business applications. They are brilliant generalists. While they have context, they often violate domain standards and compliance. Without fine-tuning, the responses may also not be consistent across users and use cases. Most enterprises are currently building their Generative AI (GenAI) applications using retrieval augmented generation (RAG) and knowledge databases. But for many, retrieval is becoming painful as the knowledge expands. At that time, having already proven value with RAG, it is easier for engineering leaders to justify investments in fine-tuning these models to gain even more efficiency and revenue gains.
Fine-tuning these LLMs for specific applications and business use cases tailors these models to be specific, sharp, and more relevant. In this academic research paper from the University of Arkansas, the researcher discovered that fine-tuning their model for movie reviews sentiment analysis using the IMDB review dataset boosted the accuracy, and the model outperformed other well-known models. The Zemoso team conducted recent experiments and discovered that fine-tuning enhances overall inference speeds. It offers better inference because the weights are adjusted to the domain-specific problems, and fewer tokens are generated, avoiding noise.
Enterprises are accelerating their artificial intelligence (AI) adoption in the form of copilots and features they are building into their digital solution for their customers. To build GenAI capabilities and keep their users engaged, they are relying on closed-source LLM application programming interfaces (APIs) or open-source models. Fine-tuning with proprietary data is often the real differentiator in these solutions.
Let’s expand these applications to include AI agents, and having a robust, well-trained knowledge database becomes even more crucial, where systems independently navigate complex tasks and autonomously make decisions. Agentic AI demands precise, context-specific intelligence to avoid inaccuracies and hallucinations, ensuring reliability and trust. Fine-tuning these models with proprietary enterprise data reduces risks related to compliance and misinformation. It also enhances continuous adaptation, explainability, and user confidence. In the end, in a self-driving AI world, a strong base of knowledge made from private data turns generalist LLMs into valuable, trustworthy, and strategic business assets.
Fine-tuning large language models (LLMs) like T5, a transformer-based model widely used for text-to-text tasks (translation, summarization, etc.), significantly improves their performance when tailored to specific applications. For example, training T5 on datasets like WMT14, a widely used multilingual dataset from the Workshop on Machine Translation (WMT) 2014 that has a lot of English-to-German translation examples, improves it at translating languages. Such targeted fine-tuning converts general-purpose models into precise, context-aware solutions.
Research proves that even with an addition of only 200 samples, accuracy can jump from 70% to 88%.
Pre-requisites and considerations for fine-tuning LLMs
As essential as fine-tuning LLMs is, it is an expensive and resource-intensive undertaking. Therefore, the Zemoso team always ensures that clients are aware of the time, cost, and technological lift the project will be for the entire team. Here are a few things to consider before embarking on the project:
High-quality, domain-specific dataset: Unlike in pretraining, fine-tuning can be effective with much smaller datasets. However, a high-quality, labeled dataset tailored to the application’s purpose is essential. For agentic AI, the data might include task-specific prompts, contextual interactions, or domain-specific terminology (e.g., medical jargon for healthcare agents). Data must be clean, representative, and sufficiently large to prevent overfitting while capturing nuances. In most instances, Zemoso has discovered that using verified enterprise data at the core and expanding it with synthetic data can be very effective when substantial amounts of company data are not available.
Computational resources: Fine-tuning LLMs requires specialized infrastructure (e.g., NVIDIA H100 GPUs handling 16GB VRAM per 1B parameters). A large-scale model like Llama 3 70B can need 24,576 GPUs and 6.4M GPU hours for full training. Developers must select architecturally optimized base models for specific applications and fine-tuning requirements. A domain-aligned evaluation framework is critical. It combines technical benchmarks (coding accuracy and, summarization ROUGE scores) with human assessments of real-world performance.
Exploring RAG vs fine-tuning
RAG (Retrieval-Augmented Generation) and fine-tuning are powerful, complementary approaches for customizing large language models (LLMs). Understanding when to employ each, or combine them, is crucial for maximizing effectiveness.
The purpose of RAG is to enhance responses with real-time, internal,internal or external data, whereas fine-tuning adapts models to specialized tasks or domains. RAG is ideal for dynamic, frequently updated sources, while fine-tuning is best for static, domain-specific datasets. Technically, RAG involves moderate complexity due to data retrieval pipelines, whereas fine-tuning requires a high level of ML expertise and infrastructure. In terms of cost, RAG typically has lower runtime expenses since it doesn't require retraining, whereas fine-tuning incurs higher upfront computational costs from GPU or TPU clusters. Regarding latency, RAG can be slower due to retrieval processes, while fine-tuning offers faster inference post-deployment.
When to use RAG: RAG excels in dynamic data environments, making it ideal for integrating real-time or frequently changing information such as live stock market data, evolving policy guidelines, or rapidly updated product catalogs without continual retraining. For instance, a financial advisory app can utilize RAG to answer customer queries about real-time stock prices or cryptocurrency fluctuations effectively. Additionally, RAG addresses broad knowledge needs by effectively synthesizing complex queries from multiple diverse sources. An example would be a legal assistant chatbot that analyzes new cases by referencing the latest judicial precedents and regulatory changes. RAG is also preferable when facing budget constraints, as it avoids GPU-intensive model retraining. For example, a small e-commerce site could use a standard API-driven LLM enhanced via RAG to provide personalized, real-time product recommendations without significant infrastructure investments.
When to use fine-tuning: Fine-tuning is essential for domain specialization, specialization—, achieving precision in specialized industries by training models on domain-specific terminology and contexts. For instance, healthcare models fine-tuned on medical literature can deliver highly accurate medical diagnoses or treatment recommendations. Fine-tuning is also crucial for structured outputs, especially when consistent and predictable responses in structured formats like JSON are required for integration with other systems. APIs designed for backend systems that must consistently return structured financial summaries or technical specifications benefit significantly from fine-tuning. Moreover, fine-tuning is critical for ensuring regulatory compliance, as it ensures responses strictly adhere to industry or regulatory standards. A healthcare chatbot fine-tuned to comply with HIPAA regulations is an example, ensuring patient interactions follow compliant data-handling practices.
Hybrid approach: retrieval-augmented fine-tuning (RAFT): By combining RAG and fine-tuning, enterprises can significantly enhance model accuracy and reliability. The hybrid approach involves initially fine-tuning a model on comprehensive domain-specific datasets and subsequently deploying RAG at inference time to access the latest external information. For example, a pharmaceutical chatbot fine-tuned on clinical trial data can dynamically utilize RAG to reference the latest FDA drug approvals or clinical guidelines, substantially reducing inaccuracies and hallucination risks. This hybrid approach typically achieves a higher12–18% accuracy improvement over standalone methods, offering an optimal balance of precision and freshness.
Decision checklist: When choosing between RAG and fine-tuning, Zemoso considers data volatility: static data aligns better with fine-tuning, while dynamic data favors RAG. Resource availability also plays a role, limited ML resources benefit from RAG, whereas robust enterprise infrastructure supports hybrid approaches. Finally, response speed requirements are crucial: immediate, sub-second latency needs are best met with fine-tuning, while scenarios allowing slight delays can effectively leverage RAG. By thoughtfully choosing between RAG, fine-tuning, or their hybrid (RAFT), enterprises can strategically enhance their LLM capabilities, ensuring both accuracy and adaptability.
How to fine-tune an LLM model?
Basic hyperparameter tuning: This process involves optimizing parameters like learning rate, batch size, number of epochs, and sequence length to enhance an LLM's performance on specific language tasks. Benefits include improved accuracy, context relevance, and faster inference, without needing the extensive resources for full retraining. However, tuning optimal hyperparameters often demands iterative experimentation and careful monitoring. In an offshore rig, hyperparameter tuning of LLMs can significantly enhance applications such as automated report generation, real-time anomaly detection, and field workforce support. For instance, a well-tuned LLM can power an analytics solution that makes accurate predictions for equipment maintenance, creates safety instructions that are tailored to the situation, and gives accurate suggestions for fixes. This can boost operational efficiency, safety compliance, and decision-making capabilities for offshore rig and plant personnel.
Instruction fine-tuning: This approach trains the model using examples that illustrate how it should respond to specific queries. Pros include improved interpretability and task-specific performance, while cons involve the necessity for high-quality instruction data. For example, in customer service, this approach can be used to train chatbots on specific responses for resolving common queries about billing and service contracts.
Full fine-tuning: It updates all of the model's weights, achieving optimal performance for the intended task. However, it requires substantial computational resources and extensive data. In B2B knowledge management, full fine-tuning might involve training a model to deeply understand and respond accurately to complex queries about industry-specific documentation and analytics. There is value in exploring this approach in legal, healthcare, or financial services where the business and use cases are extremely complex.
Task-specific fine-tuning: This method adjusts a pre-trained model to excel in a particular task or domain, boosting performance in that area. A drawback is potential forgetting of previously learned tasks. For instance, fine-tuning can help automate responses in specialized field staff support interactions, ensuring accuracy and relevance to customer inquiries about technical issues.
Transfer learning: This adapts a model trained on a broad dataset to more specialized tasks. It reduces training time and data requirements but may underperform if the new task significantly differs from the original data. This approach is frequently used in analytics and recommendation engines to quickly adapt a general-purpose model for summarizing complex enterprise data and internal documentation.
Multi-task learning: It involves training a model on multiple tasks simultaneously, improving versatility and reducing forgetting. It requires careful balancing and task selection. An example could be simultaneously training a model to automate customer support queries, perform analytics on service data, and manage knowledge databases, resulting in cohesive performance across tasks. This would be a single model that can be a resource for support agents and customers.
Sequential fine-tuning: This training approach gradually adapts a model through a series of related tasks. Pros include controlled adaptation without forgetting previous knowledge, though it requires meticulous task ordering and can be time-consuming. For example, initially training a model for automated patient interactions and gradually extending its capabilities to perform advanced analytics and knowledge extraction from internal databases to provide recommendations to service providers.
Parameter-efficient fine-tuning (PEFT): This approach updates only a subset of a model's parameters, making it memory and computationally efficient. Its limitation is potentially lower performance compared to full fine-tuning. This method could be used effectively for enhancing existing B2B chatbots to better handle specific customer queries without significant additional investment.
Supervised fine-tuning: This method involves training the model on carefully labeled datasets tailored specifically to the desired task, resulting in high accuracy and reliability. However, obtaining these labeled datasets can be resource-intensive. For instance, in medical coding, supervised fine-tuning allows engineers to train language models on vast datasets of clinical notes explicitly annotated by expert coders. This helps ensure that the model precisely identifies medical diagnoses, procedures, and treatments, accurately assigning standardized codes (like ICD-10 or CPT codes) for billing, claims processing, and compliance audits.
Few-shot learning: This method leverages only a small number of examples to quickly adapt models to new tasks without extensive retraining, making it efficient but sometimes less accurate than methods requiring large datasets. For example, in inventory management for a manufacturing company, few-shot learning can rapidly prototype a predictive system that classifies spare parts or components into specific inventory categories. The model can train on labeled examples for newly introduced or rarely stocked items and quickly start supporting inventory categorization, enabling the business to streamline inventory processes without waiting for extensive historical data.
Domain-specific fine-tuning: This method adapts language models to understand and generate content specific to a particular industry or field. While it enhances accuracy and relevance within that domain, it can limit broader applicability. For example, a large legal company can fine-tune a model specifically for paralegal research enablement by training it on extensive legal documentation, case law, and court precedents. This fine-tuned model could then accurately interpret complex legal queries, rapidly summarize relevant case histories, and highlight critical precedents. Such an approach accelerates the paralegal research process and improves efficiency and accuracy in preparing legal documents.
Feature extraction: Leverages a pre-trained model to identify and isolate relevant features from raw data, without fully retraining the entire model. Its key benefit is significantly reducing computational effort and training time, though it may not always capture nuanced task-specific patterns. In predictive maintenance for a construction firm, feature extraction helps by processing sensor and equipment data to isolate critical indicators. It isolates indicators such as vibration patterns, temperature variations, or pressure deviation without retraining the entire model. These extracted features can then feed into predictive maintenance systems to forecast equipment failures accurately, reducing downtime and maintenance expenses.
Reward modeling: This technique fine-tunes models by giving explicit rewards for desired outcomes, typically used in reinforcement learning or human-in-the-loop scenarios. Reward modeling allows targeted performance improvement aligned precisely with user-defined goals. It requires substantial computational resources and can introduce complexity due to iterative optimization cycles. For example, in medical writing, reward modeling can guide the model to generate clinical summaries or regulatory documents that consistently meet accuracy and compliance standards. It does so by rewarding clarity, factual correctness, and adherence to medical guidelines. In the insurance industry, a similar technique can be applied to reward models that generate accurate risk assessments by an underwriter. The best recommendations will generate risk assessments aligned with expert human judgments, improve efficiency, and reduce human effort over time.
Examples and applications how fine-tuned LLM models can work with RAG to deliver results
Knowledge databases and retrieval-augmented generation (financial services): Imagine a wealth management firm struggling to quickly retrieve personalized financial details for high-net-worth clients. Advisors might feel overwhelmed by scattered client histories, compliance guidelines, and dynamic market data. In this scenario, fine-tuning an LLM with retrieval-augmented generation (RAG) could create a conversational knowledge base. Advisors could then instantly access personalized client information, making better-informed advisory decisions and improving client relationships.
Customer service automation (high-ticket retail sales): Picture a luxury automobile brand whose sales team is unable to swiftly provide detailed product information, hindering their ability to close deals confidently. The team might frequently stumble on complex queries about specifications, warranties, and competitor offerings. A fine-tuned LLM chatbot could integrate comprehensive databases, offering immediate, accurate support. This would empower sales teams to provide timely, precise answers, significantly boosting customer engagement and conversions.
Content generation and personalization (marketing SDR): Consider a SaaS startup experiencing low responses to outbound sales campaigns due to generic messaging. Their SDR teams might lack time and tools to create personalized messages effectively. Using past sales data, CRM insights, and detailed buyer personas, a fine-tuned LLM could dynamically personalize messages with RAG, taking into account LinkedIn profiles and industry trends. This approach could substantially increase email response rates and meeting bookings.
Data analysis and decision-making (insurance industry): Insurance companies must effectively analyze vast amounts of historical claim data to accurately predict homeowner insurance costs. A fine-tuned LLM could quickly put together historical claims, property inspections, and regional risks to look at how that organization has behaved in the past and give insurance agents good advice. This AI-driven approach could enable precise predictions, streamline underwriting, and accelerate the quoting process.
Human-in-the-loop workflows (healthcare clinical research): Clinical research organizations take on tedious and resource-intensive inspections and compliance checks because they have to do a lot of paperwork by hand repeatedly. Document writers spend excessive time referencing regulatory documents, previous audits, and trial protocols. Fine-tuning an LLM with contextual templates to power a document writing application could provide real-time AI-generated recommendations during inspections. Such an implementation could significantly reduce inspection times, increase compliance accuracy, and enable experts to focus on strategic tasks.
Conclusion
Enterprises often find themselves at a pivotal moment when considering LLM fine-tuning. Navigating this path requires carefully balancing ambitious AI goals with available resources. Investing strategically in fine-tuning methods tailored to specific business contexts yields significant returns. Imagine customer satisfaction rising, productivity accelerating, and compliance risks fading into the background. When fine-tuning decisions align closely with enterprise objectives, the story transforms, turning general-purpose models into precision tools that unlock real-world business outcomes.
