Small language models (SLMs): a smarter way to get started with generative AI

Focused training data. Small language models train on curated datasets in your business domain, such as industry-specific content and internal documents, rather than the entire internet. This targeted approach eliminates irrelevant noise and sharpens performance for your exact use cases.

Optimized architecture. Each SLM is engineered for a specific business function. Every model has just enough layers and connections to excel at their designated tasks, which makes them outperform bulkier solutions.

Knowledge distillation. Small language models capture the essence of larger AI systems through a “teacher-student” learning process. They only take the most impactful patterns from expansive LLMs, preserving what matters for their designated tasks.

Dedicated training techniques. There are two primary training techniques that help SLMs to maintain focus:

• Pruning removes unnecessary parts of the model. It systematically trims underutilized connections from the AI’s neural network. Much like pruning a fruit tree to boost yield, this process strengthens the model’s core capabilities while removing wasteful components.

• Quantization simplifies calculation, making it run faster without demanding expensive, powerful hardware. It converts the model’s mathematical operations in a way that it uses whole numbers instead of fractions.

Gemma is an optimized version of Google’s Gemini

GPT-4o mini is a distilled version of GPT-4o

Phi is an SML suit from Microsoft that contains several small language models

Granite is the IBM’s large language model series that also contains domain-specific SLMs

Lower entry barriers. Small language models require minimal infrastructure as they can run on existing company hardware. This eliminates the need for costly GPU clusters or cloud fees.

Faster ROI. With deployment timelines measured in weeks rather than months, SLMs deliver tangible value quickly. Their lean architecture also permits rapid iterations based on real-world feedback.

Data privacy and compliance. Unlike cloud-based LLMs, small language models can operate entirely on-premises or in private cloud environments, keeping sensitive data within corporate control and simplifying regulatory compliance.

Task-specific optimization. Trained for focused use cases, SLMs outperform general-purpose models in accuracy, as they don’t contain any irrelevant capabilities that could compromise performance.

Future-proofing. Starting with small language models builds internal AI expertise without overcommitment. As needs grow, these models can be augmented or integrated with larger systems.

Reduced hallucination. Their narrow training scope makes small language models less prone to generating false information.

Customer support automation. SLMs handle frequently asked questions, ticket routing, and routine customer interactions across email, chat, and voice channels. They reduce the workload on human employees while responding to customers 24/7.

Internal knowledge base assistance. Trained on company documentation, policies, and procedures, small language models serve as on-demand assistants for employees. They provide quick access to accurate information for HR, IT, and other departments.

Support ticket classification prioritization. Unlike generic models, small language models fine-tuned on historical support tickets can categorize and prioritize issues more effectively. They make sure that critical tickets are processed promptly, reducing response times and increasing user satisfaction.

Multilingual support in resource-constrained environments. SLMs enable basic translation in areas where cloud-based AI may be impractical, like in offline or low-connectivity settings, such as manufacturing sites or remote offices.

Regulatory document processing. Industries with strict compliance requirements use small language models to review contracts, extract key clauses, and generate reports. Their ability to operate on-premises makes them ideal for handling sensitive legal and financial documentation.

Identify high-impact use cases. Focus on repetitive, rules-based tasks where specialized AI excels, such as customer support ticket routing, contract clause extraction, HR policy queries, etc. Avoid over-engineering; start with processes that have measurable inefficiencies.

Evaluate data readiness. SLMs require clean, structured datasets. Review the quality and accessibility of your data, such as support tickets and other internal content. If your knowledge base is fragmented, prioritize cleanup before model training. If a company lacks internal expertise, consider hiring an external data consultant to help you craft an effective data strategy. This initiative will pay off in the future.

Secure stakeholder buy-in. Engage department heads early to identify pain points and define success metrics.

Launch in a controlled environment. Deploy a small language model for a single workflow, like automating employee onboarding questions, where errors are low-stakes but savings are tangible. Use pre-trained models, such as Microsoft Phi-3 and Mistral 7B, to accelerate deployment.

Prioritize compliance and security. Opt for private SLM deployments to keep sensitive data in-house. Involve legal and compliance teams from the start to address GDPR, HIPAA, and AI transparency.

Set clear KPIs. Track measurable metrics like query resolution speed, cost per interaction, and user satisfaction.

Expand to adjacent functions. Once validated in one domain, apply the model to other related tasks.

Build hybrid AI systems. Create an AI system where different AI-powered tools cooperate together. If your small language model can accomplish 80% of the tasks, reroute the remaining traffic to cloud-based LLMs or other AI tools.

Support your employees. Train teams to fine-tune prompts, update datasets, and monitor outputs. Empowering non-technical staff with no-code tools or dashboards accelerates adoption across departments.

Monitor performance rigorously. Track error rates, user feedback, and cost efficiency. Use A/B testing to compare small language model outputs to human or LLM benchmarks.

Retrain with new datasets. Update models regularly with new documents, terminology, support tickets, or any other relevant data. This will prevent “concept drift” by aligning the SLM with evolving business language.

Elicit feedback. Encourage employees and customers to flag inaccuracies or suggest model improvements.

Offering a team of professionals with diverse expertise. We have Gen AI consultants, software engineers, data governance experts, and an innovative R&D team.

Building transparent, auditable AI models. With ITRex, you won’t get stuck with a “black box” solution. We develop explainable AI models that justify their output and comply with regulatory standards like GDPR and HIPAA.

Delivering future-ready architecture. We design small language models that seamlessly integrate with LLMs or multimodal AI when your needs evolve—no rip-and-replace required.

Small language models are optimized for specific tasks, while large language models handle broad, creative tasks. SLMs don’t need specialized infrastructure as they run efficiently on existing hardware, whereas LLMs need cloud connection or GPU clusters. SLMs offer stronger data control via on-premise deployment, unlike cloud-dependent LLMs. Their focused training reduces hallucinations, making SLMs more reliable for structured workflows like document processing.

SLMs excel in repetitive tasks, including but not limited to customer support automation, such as FAQ handling and ticket routing; internal knowledge assistance, like HR and IT queries; and regulatory document review. They’re ideal for multilingual support in offline environments (e.g., manufacturing sites). Industries like healthcare use small language models for HIPAA-compliant patient data processing.

Yes, hybrid AI systems combine SLMs for routine tasks with LLMs for complex exceptions. For example, small language models can handle standard customer queries, escalating only nuanced issues to an LLM. This approach balances cost and flexibility.