Today we'll take a closer look at what the practical side of an AI implementation specifically means for your company.
Step 1: Set clear objectives
Before you start building, you need to know exactly what you're going to use the AI for. Without this, you'll quickly build something that looks good but doesn't actually help anyone. This prevents wasted time and ensures a return on your investment.
Practical:
- Define the problem you want to solve (and don't start with, "Oh! That's a fancy tool we should use.").
- Determine what data is needed.
- Think about concrete success criteria (such as less searching, faster responses, higher customer satisfaction, no more duplicate entry, automatic document creation, etc.).
Step 2: Selecting the Language Model (LLM)
The language model is the engine of your system. It ensures that the AI understands your question and provides an appropriate response. This is fundamental because it allows the AI to understand language and reason.
Practical:
- Pay attention to four things when making your choice:
- Quality: How good is it in language comprehension and reasoning?
- Cost: Do you pay per use (API) or run it locally/open-source?
- Privacy: Should the data leave your organization or should it remain local?
- Speed: How quickly should an answer be returned?
- Closed or open source: Paid models are often more powerful and user-friendly, but open-source models give you more freedom and control.
- Test multiple models by asking the same questions to different models and compare the answers.
- Examples of models:
- OpenAI (GPT) is strong in language, widely applicable, many integrations.
- Claude (Anthropic) is good in longer contexts, often “safer” in answers.
- Gemini (Google) is strongly integrated with Google services.
- Mistral is open source, fast and efficient, often cheaper.
- Llama 4 (Meta) is open source, suitable for local use or private cloud.
- Gemma 3 (Google open-source) is lighter, ideal for specific applications.
- Phi-4 (Microsoft) is small, high-performance and good for edge devices.
- Gwen 3 (xAI / Elon Musk) is focused on web integration and real-time applications.
- DeepSeek is strong in analytical tasks and mathematical reasoning.
- Cohere specializes in business applications such as search and classification.
- Amazon (AWS Bedrock models) is integrated into cloud environments with scalability.
- Ollama can easily run open models locally.
- Together AI offers open models available via the cloud.
Step 3: Using Frameworks
A framework is a toolbox that helps you connect AI to your data and applications more quickly. There's no need to build everything from scratch, as this allows you to arrive at a working solution much faster.
Practical:
- Choose a framework that suits your project (for example, for chatbots, document analysis or search applications).
- Connect your data to the model using a framework to easily connect documents, databases, or APIs.
- Build step by step, starting small (such as with just one data source) and then expanding to include multiple sources or features.
- Examples of frameworks:
- LangChain is very flexible and popular; ideal for building complex AI flows.
- LlamaIndex is strong in linking documents and data sources to an AI.
- Haystack is useful for search solutions and question-and-answer systems.
- txtai is lightweight and simple, good 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 tables. This way, the AI can provide answers based on your own knowledge, not just what it already knows.
Practical:
- Retrieve your data from websites, PDFs, and tables.
- Make the data usable by removing clutter and converting the content to plain text.
- Provide structure by providing metadata such as title, date or source.
- Examples of tools that do this for you:
- Crawl4AI automatically retrieves website content.
- FireCrawl extra strong in scraping entire websites.
- ScrapeGraphAI uses AI to scrape complex websites clearly.
- MegaParser neatly converts raw documents and tables.
- Docling reads PDFs and Office files.
- LlamaParse separates tables and scanned documents well.
- Extract Thinker helps clean and structure unstructured data.
Step 5: Convert text to numbers (embeddings)
To intelligently search texts, the AI must first convert them into meaningful number sequences. This allows the AI to search not only for words, but also for the meaning behind them.
Practical:
- Break up text by dividing long documents into smaller blocks (per paragraph or chapter), so the AI can search them specifically by topic.
- Convert each piece into a number sequence. Use an embedding model that translates the text into vectors containing the meaning.
- Also preserve the context by storing additional information along with those vectors, such as title, date, or author, so you can filter better or justify answers later.
- Examples of tools and models for embeddings:
- Nomic is open-source and useful for visually analyzing vectors.
- SBERT is widely used for semantic search applications.
- OpenAI is easy to use and performs well for general embeddings.
- Voyage AI is strong in accurate and high-quality embeddings.
- Google offers embeddings via Vertex AI and other cloud services.
- Cohere specializes in business applications and multilingualism.
Step 6: Store data in a memory (vector database)
The converted texts need to be stored somewhere so the AI can quickly search them later. This memory is called a vector database. This allows the AI to quickly and accurately find the right piece of information.
With a traditional search function (like in Word or Excel), you only get results if you type "bicycle" exactly. But with vector search, the AI understands that "bicycle," "bicycle," and even "mountain bike" are related. That's why this step is necessary.
Practical:
- Choose a system that is vector-compatible and scalable enough for your data.
- Save the vectors together with metadata (title, date, source) so that the AI can always show context.
- Examples of vector databases:
- Chroma is simple and ideal for testing and small projects.
- Pinecone is scalable and cloud-based, useful for production.
- Qdrant is open source, strong in filtering options and fast searching.
- Weaviate is a flexible vector database with built-in AI functions.
- Milvus is enterprise-oriented, highly scalable.
- Postgres is a relational database with vector extensions, good for those who already use Postgres.
- Cassandra is suitable for large amounts of data spread over multiple servers.
- OpenSearch is an extension to Elasticsearch with vector search.
Step 7: Evaluate and adjust
Once the system is working, the work doesn't stop. You have to test, measure, and improve. This way, you can be sure the answers are reliable and the AI continues to perform well.
Practical:
- Test with real-life questions and see if the answers are correct.
- Collect user feedback by having people indicate whether an answer was helpful or not.
- Use the test results to clean up data or adjust settings.
- Examples of evaluation tools:
- Ragas measures how well answers match expected results.
- TruLens analyzes whether the AI uses the correct context and how relevant it is.
- Giskard tests AI systems for accuracy, reliability, and potential errors.
Step 8: Secure and Deploy
Putting AI into production also means ensuring security, privacy, and proper governance. Only then can users trust the solution, and you avoid issues surrounding sensitive data.
Practical:
- Protect sensitive data so that confidential information cannot be easily shared or misused.
- Maintain control and oversight by documenting who has access. Log which questions and answers are processed, thus ensuring transparency.
- Start with a limited group of users, learn from their usage, and then scale up safely.
- Examples of approaches and tools:
- Audit logs to keep track of all interactions.
- Access control with roles and rights (who can do what?).
- Monitoring & alerts tools that continuously monitor performance, costs and abuse.
Final AI application flow
- The end user asks a question in your application
For example, in a chatbot, search box or helpdesk app. - The question goes to the vector database
The query is converted into a vector (step 5), and the database searches for the most relevant pieces of information (step 6). - The relevant pieces are returned to the language model (LLM)
The LLM combines the user's question with the context found. This allows the model to provide an answer that's consistent with your data, not just its general knowledge. - The answer appears in the application
The user sees a clear answer, possibly with a source reference or link to the original document.
So you see, building an AI system with LLMs isn't magic. It's just a series of logical steps. And at Canyon Clan, we can help you every step of the way.