How Enterprises Can Operationalise Large Language Models at Scale
AI
20th April 2025
Invos Global
The Opportunity
Large Language Models (LLMs) power chatbots, code‑assistants, and knowledge search. Yet many enterprises stall at pilot phase due to governance, cost, and latency concerns.
Five‑Step Framework
1. Use‑Case Prioritisation – map business value against data‑privacy risk.
2. Data‑Pipeline Hardening – cleanse and label internal documents; implement access control.
3. Fine‑Tuning vs. Prompt‑Engineering – decide whether to fine‑tune or rely on advanced prompting.
4. Inferencing Optimisation – deploy quantised models on GPUs for high throughput, or leverage CPU‑int8 for cost‑constrained scenarios.
5. Monitoring & Feedback – track hallucination rates and integrate feedback loops.
Real‑World Case
A Sri Lankan bank deployed an on‑prem LLM to power tri‑lingual (Sinhala/Tamil/English) knowledge search for 2 500 employees. After fine‑tuning on policy manuals, average query‑resolution time dropped from eight minutes to 90 seconds.
Key Takeaways
• Start with a tightly scoped knowledge‑search use case.
• Build a cross‑functional “LLM task‑force.”
• Plan for quarterly fine‑tuning cycles.
Related Blogs
