This content originally appeared on DEV Community and was authored by Kamal Rawat

The AI Paradox: You Have the Model, But Do You Know the Problem?

In our last article, we pulled back the curtain on AI models. We learned that more parameters don’t automatically mean a better or smarter solution, and a bigger model can come with a hidden “AI tax” on your budget.

But before you even choose a model, here’s the bigger question:

1. Do you truly understand your business problem?
2. Why Do We Even Need AI Models ?

This article isn’t about the tech; it’s about the strategy.

Businesses today are data-rich but insight-poor. From retailers handling millions of transactions to logistics firms tracking shipments worldwide, data is exploding faster than companies can interpret it.

AI models turn this chaos into clarity. They help companies by:

• Retail & E-commerce: Forecasting demand so shelves aren’t empty or overstocked. For example, Walmart uses AI-driven demand prediction to cut excess inventory and save millions annually.
• Finance: Detecting fraud in real-time by spotting unusual transaction patterns that humans or rules-based systems would miss. JPMorgan’s fraud detection AI saves the bank millions each quarter.
• Insurance: Automating claims processing by reading documents, classifying damage categories, and reducing human turnaround time from days to hours.
• Healthcare: Analyzing X-rays or lab reports faster than radiologists in some cases, enabling earlier intervention and improved patient outcomes.

Whether powered by a large general-purpose model or a small, domain-specific one, the goal is the same: turning raw data into actionable business outcomes.

Before we continue further, Minor acknowledgement that models exist on a continuum, not just two buckets (Small or Large).

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While models exist across a range of sizes, for simplicity we’ll compare two ends of the spectrum: small, task-specific models vs. large, general-purpose models.

The Core Trade-off: Small vs Large Models

1. Small, Specialized Models

   • Trained or fine-tuned for a narrow task (e.g., contract clause extraction, sentiment analysis, medical diagnosis).
   • Lower cost, faster inference, easier to deploy on edge devices or within compliance-restricted environments.
   • Usually weaker in general reasoning, multi-step logic, or unexpected queries.

2. Massive, General-Purpose Models (GPT-4, Claude, Gemini, etc.)

   • Trained on broad internet-scale data, so they’re versatile across many domains.
   • Strong at multi-step reasoning, handling ambiguity, combining context.
   • Costly, compute-heavy, and sometimes “overkill” if you only need narrow answers.

Lets take a scenario where there is RAG(Retrieval-Augmented Generation) pipeline attached to LLM. Lets break it down:

1. Vector Database
   • Stores your company’s documents as embeddings.
   • On query, it retrieves the most relevant chunks (knowledge grounding).
2. LLM(Small or Large)
   • Takes the retrieved chunks.
   • Generates a natural, contextually accurate response.

The Key Question: Is a Small Model Enough?

Yes, small models can be enough if:

• Your queries are narrow and predictable (e.g., “show me the policy clause,” “extract invoice total”).
• The retrieved chunks already contain the answer in a clean format.
• You mainly need language fluency to stitch together responses from your data.
• You care about cost efficiency and want to scale cheaply.

But larger models are valuable when:

• The query requires reasoning beyond retrieval, e.g., “Compare the risk posture of Policy A vs Policy B based on clauses”.
• Users may ask ambiguous, incomplete, or tricky questions that need interpretation.
• You need multi-hop reasoning (e.g., combining insights across multiple retrieved documents).
• The data retrieved is messy, incomplete, or requires contextual stitching.

Real-World Example

• Small model case:
  You ask: “What’s the interest rate in Contract #123?”

  • The vector DB retrieves the exact clause.
  • A small LLM (even 7B) can read that snippet and answer perfectly.

• Large model case:
  You ask: “Across all ~2500 contracts, which clients have the most favorable early termination rights, and what risk does that pose to revenue forecasts?”

  • Requires pulling from many documents, understanding legal language nuances, and connecting business implications.
  • A larger LLM is much more reliable here.

Strategic Answer

• If your use case is structured, retrieval-heavy, and domain-specific, Small specialized LLM (cheaper, faster).

• If your use case requires reasoning, interpretation, multi-step synthesis then Larger general-purpose LLM (better accuracy).

Many companies use a hybrid approach:

• Use small LLMs for 80% of simple, repetitive queries.
• Fall back to larger LLMs only when complexity is high. (This is called an orchestration strategy—think of it as a “model router.”)

This isn’t a one-size-fits-all problem. What’s the most complex business problem you’ve seen that AI could solve? Share your thoughts below!

AIstrategy #BusinessLeader #LLM

This content originally appeared on DEV Community and was authored by Kamal Rawat