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How to Build AI Systems with LLMs

Today we zoom in on what the practical side of an AI implementation specifically means for your business.

Step 1: Define Clear Objectives

Before you start building, you need to know exactly what you will use the AI for. Without this, you will quickly build something that looks impressive, but doesn’t actually help anyone. You prevent wasted time and ensure the investment delivers a return.

Practical:

  • Define the problem you want to solve (and don’t start with “Oh! That’s a fancy tool we just have to use.”).
  • Establish what data is required.
  • Define concrete success criteria (such as less search time, faster response times, higher customer satisfaction, eliminating duplicate data entry, automated document generation, etc.).

Step 2: Choose the Language Model (LLM)

The language model is the engine of your system. It ensures that the AI understands what you ask and provides an appropriate response. This is fundamental, as it allows the AI to comprehend language and reason.

Practical:

  • Pay attention to four factors when making your choice:
    • Quality: how proficient is it in language comprehension and reasoning?
    • Cost: do you pay per usage (API) or run it locally / open-source?
    • Privacy: can data leave your organization or must it remain on-premises?
    • Speed: how quickly must a response be returned?
    • Closed vs. open source: proprietary models are often more powerful and user-friendly, but open-source models offer greater freedom and control.
    • Test multiple models by submitting identical prompts to different models and comparing their answers.
  • Examples of models:
    • OpenAI (GPT) is strong in language, broadly applicable, with extensive integrations.
    • Claude (Anthropic) excels in longer contexts, often “safer” in its responses.
    • Gemini (Google) is deeply integrated with Google services.
    • Mistral is open-source, fast, and efficient, often more cost-effective.
    • Llama 4 (Meta) is open-source, well-suited for local deployment or private clouds.
    • Gemma 3 (Google open-source) is lightweight, ideal for specialized applications.
    • Phi-4 (Microsoft) is compact, performant, and well-suited for edge devices.
    • Gwen 3 (xAI / Elon Musk) is focused on web integration and real-time applications.
    • Cohere specializes in enterprise applications such as search and classification.
    • Amazon (AWS Bedrock models) is integrated into cloud environments with high scalability.
    • Ollama can easily run open models locally.
    • Together AI offers open models accessible via the cloud.

Step 3: Use Frameworks

A framework is a toolkit that helps you connect the AI to your data and applications faster. You don’t have to build everything from scratch, allowing you to arrive at a working solution much more rapidly.

Practical:

  • Choose a framework that aligns with your project (for example, for chatbots, document analysis, or search applications).
  • Connect your data to the model using a framework to easily plug in documents, databases, or APIs.
  • Build out step by step: start small (such as with a single data source) and expand later with multiple sources or capabilities.
  • Examples of frameworks:
    • LangChain is highly flexible and popular; ideal for constructing complex AI flows.
    • LlamaIndex excels at connecting documents and data sources to an AI.
    • Haystack is convenient for search solutions and question-and-answer systems.
    • txtai is lightweight and straightforward, great for semantic search and classification.

Step 4: Collect and Prepare Data

An AI can only function if you feed it the right information. This can range from manuals and reports to web pages and spreadsheets. That way, the AI can deliver answers based on your proprietary knowledge, rather than just what it already knows.

Practical:

  • Extract your data from websites, PDFs, and spreadsheets.
  • Make the data usable by removing noise and converting content into plain text.
  • Provide structure by attaching metadata, such as title, date, or source.
  • Examples of tools that handle this for you:
    • Crawl4AI automatically extracts website content.
    • FireCrawl is especially strong at scraping entire websites.
    • ScrapeGraphAI leverages AI to scrape complex websites in an orderly fashion.
    • MegaParser neatly converts raw documents and tables.
    • Docling parses PDFs and Office documents.
    • LlamaParse cleanly separates tables and scanned documents.
    • Extract Thinker assists in cleaning and structuring unstructured data.

Step 5: Convert Text into Numbers (Embeddings)

To search texts intelligently, the AI must first convert them into numerical sequences that capture meaning. This allows the AI to search not just for keywords, but for the meaning behind the words.

Practical:

  • Chunk the text by breaking long documents into smaller blocks (by paragraph or chapter), allowing the AI to search targeted topics.
  • Convert each chunk into a sequence of numbers. Use an embedding model that translates text into vectors containing semantic meaning.
  • Preserve context by saving additional metadata alongside vectors, such as title, date, or author, making it easier to filter or justify answers later.
  • Examples of tools and models for embeddings:
    • Nomic is open-source and convenient for visually analyzing vectors.
    • SBERT is widely used for semantic search applications.
    • OpenAI is user-friendly and delivers high performance for general embeddings.
    • Voyage AI is strong in accurate, high-grade embeddings.
    • Google offers embeddings via Vertex AI and other cloud services.
    • Cohere specializes in enterprise applications and multilinguality.

Step 6: Store Data in Memory (Vector Database)

The converted texts must be stored somewhere so the AI can rapidly search through them later. We call that memory a vector database. It enables the AI to find the right piece of information very quickly and with targeted precision.

With traditional search (such as in Word or Excel), you only get results if you type the exact word “bicycle”. But with vector search, the AI understands that “bicycle”, “bike”, and even “mountain bike” are semantically related. That is why this step is essential.

Practical:

  • Choose a system suited for vectors and scalable enough for your data volume.
  • Store vectors alongside metadata (title, date, source) so the AI can always present context.
  • Examples of vector databases:
    • Chroma is straightforward and ideal for testing and small projects.
    • Pinecone is scalable and cloud-based, convenient for production.
    • Qdrant is open-source, strong in filtering capabilities and fast search.
    • Weaviate is a flexible vector database with built-in AI capabilities.
    • Milvus is enterprise-focused and highly scalable.
    • Postgres is a relational database with vector extensions, great for teams already using Postgres.
    • Cassandra is well-suited for massive datasets distributed across multiple servers.
    • OpenSearch is an extension of Elasticsearch featuring vector search.

Step 7: Evaluate and Refine

Once the system works, the job isn’t done. You must test, measure, and improve. That way, you know for sure that answers are reliable and that the AI continues to perform well.

Practical:

  • Test with real-world questions that arise in practice and check if the answers are accurate.
  • Collect feedback from users by having people indicate whether an answer was helpful or not.
  • Use test results to clean up data or adjust settings.
  • Examples of evaluation tools:
    • Ragas measures how well responses match expected results.
    • TruLens analyzes whether the AI utilizes the proper context and how relevant it is.
    • Giskard tests AI systems for accuracy, reliability, and potential errors.

Step 8: Secure and Deploy

Bringing an AI into production also means ensuring security, privacy, and proper management. Only then can users trust the solution, and you prevent issues around sensitive data.

Practical:

  • Protect sensitive data so confidential information cannot be inadvertently shared or misused.
  • Maintain control and oversight by establishing access controls. Log which queries and responses are processed, ensuring full transparency.
  • Start with a limited group of users, learn from their usage, and safely scale up afterwards.
  • Examples of approaches and tools:
    • Audit logs for recording all interactions.
    • Access management with roles and permissions (who is allowed to do what?).
    • Monitoring & alerts tools that continuously supervise performance, costs, and anomalous activity.

Final AI Application Flow

  1. The end user asks a question in your application
    For instance, in a chatbot, search window, or helpdesk app.
  2. The question is sent to the vector database
    The question is converted into a vector (step 5), and the database searches for the most relevant information chunks (step 6).
  3. The relevant chunks are returned to the language model (LLM)
    The LLM combines the user’s question with the retrieved context. This allows the model to provide an answer that aligns with your data, not just its general knowledge.
  4. The answer appears in the application
    The user sees a clear response, optionally with citations or links to the original document.

So as you can see, building an AI system with LLMs isn’t magic. It’s simply a series of logical steps. And at Canyon Clan, we can guide you forward at every single step.

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