Question 1

What is the difference between integrating an existing LLM (like GPT or Claude) and training a custom AI model?

Accepted Answer

Integrating an existing Large Language Model (LLM) means using an API service from providers like OpenAI (GPT-4), Anthropic (Claude), or Google (Gemini) to add conversational AI, document summarization, content generation, or question-answering capabilities to your product. This approach is fast (weeks to deploy), cost-effective (pay-per-token rather than GPU compute), and leverages models trained on massive datasets. Training a custom model is appropriate when you have a specialized domain with proprietary data (e.g., medical imaging, financial documents, or a specific language variant), require a model that runs fully on your own infrastructure for data privacy, or need a small, fast model optimized for a single narrow task. In most business applications, LLM integration delivers 90% of the value at 10% of the cost and complexity of custom training.

Question 2

What is RAG (Retrieval-Augmented Generation) and when should my business use it?

Accepted Answer

Retrieval-Augmented Generation (RAG) is an architecture that enhances an LLM with access to your own proprietary documents, databases, or knowledge bases. Instead of relying solely on the model's training data, the system first retrieves the most relevant content from your data store (using vector search) and then passes that context to the LLM to generate a grounded, accurate answer. Use RAG when you want an AI chatbot or assistant that can answer questions about your specific products, policies, contracts, or internal documentation without hallucinating incorrect information. Typical RAG use cases include internal knowledge management systems, customer support bots trained on your FAQ and product docs, legal document Q&A, HR policy assistants, and technical documentation search. TechYantram builds end-to-end RAG systems including document ingestion pipelines, vector database setup (Pinecone, Weaviate, or pgvector), and conversational frontend interfaces.

Question 3

How much does it cost to run an AI-powered feature in production?

Accepted Answer

AI inference costs vary significantly by model and usage volume. For LLM API integrations, costs are token-based. GPT-4o runs approximately $0.0025 per 1,000 input tokens and $0.01 per 1,000 output tokens. Claude Sonnet is similarly priced. For a typical customer support chatbot handling 10,000 conversations per month (averaging 500 tokens per exchange), the LLM cost would be $25–$75 per month — very cost-effective. For high-volume applications (millions of API calls), switching to a smaller fine-tuned model or a self-hosted open-source model (Llama 3, Mistral) on a GPU cloud instance can reduce costs by 60–80%. We always model your expected usage volume and recommend the most cost-efficient architecture during the proposal phase.

Question 4

What data do you need from us to build a custom ML model or AI feature?

Accepted Answer

The data requirements depend on what you want the AI to do. For a classification model (e.g., spam detection, sentiment analysis, document categorization), we typically need 1,000–10,000 labeled examples minimum — more data generally means higher accuracy. For a recommendation engine, we need historical user-item interaction data (clicks, purchases, ratings). For a demand forecasting model, we need 2+ years of historical sales or transaction data with relevant contextual variables. For LLM fine-tuning, we need 100–1,000 high-quality input/output example pairs in your desired style or domain. We conduct a data audit at the start of every AI project to assess what you have, identify gaps, and recommend a data collection or augmentation strategy if needed. We handle all data preprocessing, cleaning, and feature engineering internally.

Question 5

How do you ensure the AI system you build is accurate and trustworthy?

Accepted Answer

We apply a rigorous evaluation framework throughout the AI development lifecycle. For ML models, we use holdout test sets, k-fold cross-validation, and confusion matrix analysis to verify accuracy metrics before deployment. We monitor for data drift and model degradation in production using tools like Evidently AI and AWS SageMaker Model Monitor. For LLM applications, we use automated evaluation suites that test for hallucination, relevance, and faithfulness against ground-truth data. We also implement human-in-the-loop review checkpoints for high-stakes outputs (e.g., financial summaries, medical information). Every AI system we build includes an observability layer with logging, tracing, and alerting so you can detect and respond to quality issues in production. We define clear accuracy baselines and performance thresholds before launch and only ship when those targets are met.

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