by Siddharaj Sarvaiya

May 05, 2025

LLM Integration: 6 Questions to Ask Before You Start

If you’re exploring Large Language Model integration for your business, product, or platform, this guide is for you.

We’ll explore what LLM integration really is, what changes once you integrate, and the key questions you’ll want answered before getting started.

What Is LLM Integration? Explained with Example

LLM integration is the process of embedding a large language model into your product, platform, or workflow. That’s it.

It can be as simple as using an API to add a chat assistant. Or as complex as building an internal knowledge agent that connects to your databases, understands your operations, and answers in real-time.

Example: LLM Integration with Power BI

Power BI is already a great tool — but sometimes it can feel like you’re staring at a dashboard, slicing and dicing filters, digging through graphs, and still not finding what matters. It takes time. And even then, you’re the one connecting the dots.

Now imagine you integrate an LLM.

Instead of scrolling through reports, you just ask,

“What changed in regional sales last quarter, and why?” Or

“What’s the key metric I should be worried about today?”

The LLM pulls relevant visuals, interprets trends, summarizes patterns, and tells you the why — in plain language. You stop guessing. You start acting.

It turns Power BI from a reporting tool into a decision-making assistant. That’s the power of LLM integration.

What You Can Actually Expect from LLM Integration?

You already know LLMs can answer questions, summarize docs, and generate content. But what can they really deliver inside your platform or workflow?

It depends on how far along you are in five critical areas.

1. Knowledge and Content:

HTML Table Generator

If You Have	You Get
Structured + unstructured content linked to your use case (product docs, SOPs, emails, support logs, training material)	Answers from across formats, real-time Q&A, smart assistants for internal or customer use
Clear boundaries (what the model should know vs. ignore)	Fewer hallucinations, more trust

2. System Interfaces and Actions:

HTML Table Generator

If You Have	You Get
Well-documented internal APIs or service endpoints	Agents that can take action (update a record, fetch user data, trigger workflows)
Safe, role-based control over what can be accessed or changed	Operational copilots that reduce human steps

3. Identity and Context Awareness:

HTML Table Generator

If You Have	You Get
User-specific roles, history, permissions, goals	Personalized responses, smarter recommendations, better UX
Access to real-time session or app state	Relevant, in-context answers that feel integrated into your product

4. Repetitive Language Tasks at Scale:

HTML Table Generator

If You Have	You Get
High-volume message, report, or summary generation	Drafted content in seconds — emails, briefs, support notes, internal updates
Domain examples and tone/style patterns	Outputs that match your voice and brand with minimal edits

5. Clear Friction in Your Current Workflow:

HTML Table Generator

If You Know	You Get
Where users pause, escalate, or drop off	Targeted LLM features that unblock pain points — onboarding guides, embedded help, proactive prompts
What employees spend time on but hate doing	Agent automation that actually gets adopted

If the pieces are in place — even some of them — you can expect:

✔️ Speed: Answers, drafts, and actions that happen 10x faster

✔️ Consistency: Fewer mistakes, less variance across people

✔️ Scale: Serve more users with the same or fewer resources

✔️ Time savings: For users, agents, analysts, and devs

✔️ A better product: Because it feels smarter and more human

Thinking About LLM Integration? Ask These Questions First

If you’re considering LLM integration, you’ll have questions. A lot of them. Let’s tackle the most important ones.

1. What’s the Best Way to Integrate an LLM into System?

There are different routes:

1️⃣ API Integration: If you need speed and simplicity, the API route (e.g., OpenAI or Anthropic) is the way to go.

2️⃣ Custom Model Deployment: If you’re looking to fine-tune or control everything, running your own model or deploying through a cloud service like Azure could be better.

3️⃣ Use of Frameworks: Want to connect the LLM to your business workflows easily? Look into frameworks like LangChain or LlamaIndex.

2. Should You Fine-Tune or Just Prompt?

Prompting is usually enough. You can feed custom context using “retrieval augmented generation” (RAG), which pulls from your private data and appends it to prompts in real-time.

Fine-tuning helps when:

✔️ You want a consistent brand tone or voice.

✔️ You have highly structured, repetitive data.

✔️ Your use case needs domain-specific patterns.

But fine-tuning adds cost, complexity, and maintenance. Most teams start with prompting + RAG.

3. Can LLMs Work with Private or Sensitive Data?

Yes — but be careful.

If you’re sending data to OpenAI or Anthropic via API, check their privacy terms. Most enterprise APIs don’t use your data for training, but you still need to secure what you send.

To keep full control:

✔️ Use Azure OpenAI with your private network

✔️ Deploy open-source models on your own cloud or servers

✔️ Use encrypted vector databases to store and retrieve context

Compliance matters here. Don’t skip it.

4. How Much Does LLM Integration Cost?

It depends on your use case. Because,

➡️ Tokens: LLMs charge per token — both input and output. A “token” is roughly 3–4 characters or 0.75 words.

➡️ Calls: More users, more calls. One user prompt can lead to 2–3 model calls (retries, refinement, system prompts).

➡️ Model choice: GPT-4 is 15x more expensive than GPT-3.5. Anthropic and Claude are priced differently. Open-source models are free to use but come with infra and ops costs.

➡️ Context cost: Every time you inject a long prompt with system instructions or business data, it counts as tokens. It adds up.

Hence, plan for peak usage, average input size, and cost of retries and evaluations.

5. What Kind of Team Do You Need?

You don’t need a deep ML team to get started. But you do need:

✔️ Backend developers (to call APIs, manage data flow)

✔️ Prompt engineers or product folks who can design prompts and evaluate responses

✔️ DevOps or MLOps (if you’re self-hosting models)

✔️ Someone to test for output quality and safety

If you’re building something critical (healthcare, finance, legal), bring in someone who can review and validate outputs.

Read our detailed article on ➡️ Generative AI in DevOps

6. What Can Go Wrong in LLM Integration?

Plenty, if you’re not careful.

➡️ Bad output: The LLM sounds confident but is wrong. It may invent facts, miss nuance, or misinterpret input.

➡️ Inconsistent tone: The same prompt may give different styles or levels of detail across sessions.

➡️ Cost spike: A small feature can get expensive if you don’t monitor token usage.

➡️ Latency: GPT-4 can take 3–5 seconds per response. Bad UX if not managed well.

➡️ Token overflow: Long prompts and responses get cut off. Some models have token limits (e.g., 8k, 16k, 128k).

Not Sure Where to Start with LLM Integration?

We can help you make the right move.

Generative AI Development Services

How Do You Actually Get Started with LLM Integration?

Start lean. Build the smallest working version. Test it with real users. Improve based on what you learn. That’s how you begin — no big bang, just focused progress.

Here’s a step-by-step guide.

1. One Use Case

Pick something internal and bounded. Example:

An assistant who summarizes meeting notes
A bot that answers HR policy questions using your documents
A draft generator that writes LinkedIn posts from CRM notes

Read an insightful blog on ➡️ 12 Latest Use Cases of Generative AI for Enterprise

2. Pick a Model

There’s no one-size-fits-all, so choose based on speed, cost, and context needs:

GPT-3.5: Fast, cheap, works for most general tasks
Claude: Handles longer context, good for document-heavy use cases
GPT-4: Better reasoning but slower and more costly
Open-source (Mistral, Gemma): Use if you need more control or want to host privately

3. Choose a Pattern:

You have two basic patterns:

Simple prompt call → input → LLM → output
RAG → input → retrieve → context + input → LLM → output

4. Set Up Basic Stack:

Your basic stack can look like this:

Model API (OpenAI, Claude, etc.)
Vector DB (Pinecone, Weaviate, etc.)
Integration logic (LangChain, LlamaIndex, or your own code)

5. Monitor Everything:

Keep in mind to,

Log every input/output
Track token usage and latency
Review output quality regularly
Add a feedback loop for users

What You Need Before You Start? A Checklist

Here’s your LLM integration starter checklist:

✔️ Clear use case

✔️ Access to a reliable model (API or hosted)

✔️ Internal data (if needed) organized and accessible

✔️ Prompting and context injection strategy

✔️ Output evaluation plan

✔️ Monitoring and fallback mechanisms

✔️ User feedback loop

This is a system, not a script. Treat it like one.

Need Help with LLM Integration? Let’s Make It Happen

LLM integration can look simple on paper, but once you start, there are real decisions to make — about models, prompts, data, and use cases.

If you’re unsure where to begin or want to avoid the common pitfalls, you don’t have to figure it out alone.

Being an enterprise AI development company, we work with Generative AI every day.

We help teams figure out what makes sense to build, how to design with LLMs, and how to make sure it works in the real world.

For that, we have dedicated experts in LLMs, prompt engineering, RAG, and AI architecture — along with strong product engineering teams to bring everything together.

One example? We built SAGE, our in-house AI agent that helps employees instantly access workplace information. From leave balances to confirmation dates — SAGE answers it all in seconds.

It’s not just a chatbot. It’s a working example of what LLM integration looks like when done right.

So, if you’re thinking about integrating LLMs into your product or internal tools, and want to talk through ideas or technical options, we’re here.

Let’s connect!

Want to Integrate LLMs into Your Workflow?

Let us help you do it right.

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Siddharaj Sarvaiya

Program Manager - Azilen Technologies

Siddharaj is a technology-driven product strategist and Program Manager at Azilen Technologies, specializing in ESG, sustainability, life sciences, and health-tech solutions. With deep expertise in AI/ML, Generative AI, and data analytics, he develops cutting-edge products that drive decarbonization, optimize energy efficiency, and enable net-zero goals. His work spans AI-powered health diagnostics, predictive healthcare models, digital twin solutions, and smart city innovations. With a strong grasp of EU regulatory frameworks and ESG compliance, Siddharaj ensures technology-driven solutions align with industry standards.

LLM Integration: 6 Questions to Ask Before You Start

What Is LLM Integration? Explained with Example

Example: LLM Integration with Power BI

What You Can Actually Expect from LLM Integration?

1. Knowledge and Content:

2. System Interfaces and Actions:

3. Identity and Context Awareness:

4. Repetitive Language Tasks at Scale:

5. Clear Friction in Your Current Workflow:

Thinking About LLM Integration? Ask These Questions First

1. What’s the Best Way to Integrate an LLM into System?

2. Should You Fine-Tune or Just Prompt?

3. Can LLMs Work with Private or Sensitive Data?

4. How Much Does LLM Integration Cost?

5. What Kind of Team Do You Need?

6. What Can Go Wrong in LLM Integration?

How Do You Actually Get Started with LLM Integration?

1. One Use Case

2. Pick a Model

3. Choose a Pattern:

4. Set Up Basic Stack:

5. Monitor Everything:

What You Need Before You Start? A Checklist

Need Help with LLM Integration? Let’s Make It Happen

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