LLM Services
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Data Preparation for AI
With new Large Language Models (LLMs) coming out every now and then, you might be wondering which LLM is the most suitable for your use case. There are various factors to consider when choosing an LLM.
We provide end-to-end services from model selection to deployment and sustainment.
Model Size and Performance
Domain-Specific Training
Bias (Political, Cultural etc.)
Support and Update Frequency
Privacy (for API based LLMs)
Cost
LLM Customization
Once you have selected a Large Language Model (LLM) for your use case, you will need to customize it for it to give you unhalucinated, useful information. The customization will depend on your specific use case and industry. Here are the steps typically taken:
Define Objectives & Use Cases
Customer Support, Content Generation, Code Generation, Coding Assistance, Domain Specific Research
Select the Right Base Model
Oper Source vs. Proprietary Models, Cost, Latency & Performance
Retrieval Augmented Generation (RAG) and Embeddings
Use private knowledge to improve LLM's response. Use Vector databases to save business specific knowledge.
Data Collection and Data Wrangling
Domain specific data, Pipelines, Data Cleansing, Normalize to remove any biases
LLMOps and AI Governance
Logging and Monitoring, Prevent biases & hallucinations, Ensure Compliance, Updates and Retraining
Fine-tuning (when necessary)
Supervised Fine-Tuning, Reinforcement Learning, Low Rank Adaptation
Prompt Engineering
Provide Context, Code Sample, Writing Styles, Company Culture etc. to improve LLM output.
Feedback and Refinement
Obtain feedback from users and retrain the model as required.
True power of AI can only be harnessed iteratively, not surgically.