Fine Tune LLMs To Reduce Token Cost: RAG, CAG, Memory Cache
Jini Enterprise helped leading middle East Healthcare company to reduce LLM costs by using state-of-the-art techniques to reduce token cost and context window management
Case Study: How Jini Enterprise Reduced Frontier Model Costs for a Leading Middle East Healthcare Provider
A prominent healthcare organization in the Middle East integrated Generative AI to assist medical staff with patient inquiries, appointment scheduling, and clinical decision support. However, relying on powerful "Frontier" models (such as GPT-4 and Claude 3) created a massive financial burden due to high token pricing. Jini Enterprise intervened with a state-of-the-art optimization framework, leveraging RAG, CAG, and Memory Caching to slash operational costs while maintaining the rigorous accuracy required in the medical field.
The Challenge: High Costs and Context Management in Healthcare
The client was running a sophisticated Gen AI setup where every interaction required processing vast amounts of medical data. This led to several critical operational bottlenecks:
- Frontier Model Token Bloat To ensure accuracy, the system was feeding entire patient histories and large medical protocol documents into the LLM context window for every single query. This "brute force" context management resulted in exorbitant token consumption and unsustainable monthly bills.
- Latency in Patient Interactions The sheer volume of text being processed by these large models caused significant delays. In a healthcare setting, where patients and doctors require immediate answers, the slow response times were negatively impacting user trust and adoption.
- Repetitive Processing Waste A significant portion of inquiries—such as visiting hours, insurance eligibility, or standard drug interaction questions—were identical. Yet, the system re-processed these through the expensive Frontier model every time, wasting resources on redundant computations.
The Solution: Jini Enterprise's "Fine-Tune & Optimize" Framework
Jini Enterprise deployed a multi-layered architecture designed to manage the context window efficiently and reduce the reliance on raw model intelligence. The solution focused on three key technical pillars:
We restructured the data pipeline to index the client's vast library of medical journals and internal operational guidelines. Instead of dumping full documents into the prompt, the system now retrieves only the specific paragraphs relevant to the medical query. This reduced the input prompt size by over 80%, ensuring the Frontier model only processes essential data.
For patient-specific queries, we implemented CAG to dynamically inject real-time Electronic Health Record (EHR) data—such as current medications or recent lab results—directly into the context window at the moment of inference. This eliminated the need to maintain long, expensive conversation histories, keeping the token count low while maintaining high personalization.
Jini Enterprise implemented a semantic caching layer for high-volume, static inquiries. Questions about hospital policies, standard drug dosages, or administrative details are now served instantly from the cache. This bypasses the Frontier model entirely for approximately 40% of traffic, reducing token costs to zero for these interactions.
The Results: Efficiency Meets Clinical Excellence
The collaboration with Jini Enterprise delivered transformative results, proving that healthcare providers can leverage top-tier AI models without breaking the bank.
- 65% Reduction in Monthly Token Costs via optimized context management
- 3x Faster Response Times for patient inquiries due to caching
- 40% Decrease in API Calls to Frontier Models (deflected by Memory Cache)
- 100% Data Compliance maintained with localized context handling
"Jini Enterprise didn't just optimize our code; they optimized our entire AI economics. By managing our context windows and caching routine queries, we are now able to run Frontier models in production at a fraction of the previous cost, all while delivering faster answers to our patients."
Conclusion: Sustainable Gen AI for Healthcare
The successful deployment at this Middle East healthcare leader highlights a critical industry shift: the move from experimental AI to economically viable production systems. By applying advanced techniques like RAG, CAG, and Token Usage Optimization, Jini Enterprise enabled the client to harness the full power of Frontier models without the prohibitive costs. This approach ensures that Generative AI remains a scalable asset for improving patient outcomes.
Looking to reduce your LLM infrastructure costs? Contact Jini Enterprise to audit and optimize your Gen AI pipeline today.
Key Impact
Reduced token cost of using Frontier models in production