The Modern AI Stack In 2026: A Practical Guide For AI Engineers + Video

Introduction:

The modern AI application is a complex ecosystem that extends far beyond a simple API call to a large language model. In 2026, building production-ready AI involves orchestrating a diverse stack of technologies, from specialized vector databases and robust data extraction tools to sophisticated evaluation frameworks. Understanding this entire stack and when to apply each layer is what distinguishes an AI enthusiast from a true AI engineer.

Learning Objectives:

Differentiate between the seven core layers of the modern AI stack and understand their specific functions.
Learn how to select and integrate appropriate models, frameworks, and databases for various AI use cases.
Gain practical knowledge for setting up and configuring key tools across Linux and Windows environments.

You Should Know:

Setting Up a Local LLM Environment with Ollama

Running open-source models locally is a cornerstone of modern AI development, offering privacy, control, and cost-effectiveness. The ability to spin up a model like Llama 3 or Mistral on your own machine allows for rapid prototyping and testing without relying on external APIs. Ollama simplifies this process significantly by managing the model weights and providing a simple API.

Step‑by‑step guide explaining what this does and how to use it:
This guide will walk you through installing Ollama, pulling a model, and interacting with it via the command line and API.

1. Installation:

Linux/macOS: Open a terminal and run the following command. This script downloads and installs Ollama.
```
curl -fsSL https://ollama.com/install.sh | sh
```
Windows: Download the Ollama installer from the official website (ollama.com) and run it. Ollama will run as a background service.

Pulling a Model: Once installed, you can download a model. For example, to pull the efficient `phi` model, use:
```
ollama pull phi
```
This command downloads the model weights, which can be several gigabytes, so ensure you have a stable internet connection.
Running the Model Interactively: Test your model directly in the terminal:
```
ollama run phi
```
Type a prompt like “Explain the concept of a vector database” and press Enter. You’ll see the model generate a response.
Using the API: Ollama provides a REST API, making it easy to integrate with your applications. While the model is running, you can send a POST request to `http://localhost:11434/api/generate`.
```
curl http://localhost:11434/api/generate -d '{
"model": "phi",
"prompt": "Why is data extraction important for RAG?",
"stream": false
}'
```
The `”stream”: false` parameter ensures you get a single JSON response. This API endpoint is crucial for integrating the model into a larger application workflow.
Implementing a RAG Pipeline with LangChain and Chroma

LangChain serves as the primary orchestrator for AI workflows, while vector databases like Chroma provide the long-term memory necessary for Retrieval-Augmented Generation (RAG). A RAG pipeline enhances an LLM’s responses by grounding them in your specific data, reducing hallucinations and improving factual accuracy. This combination allows you to build an application that can “chat with your documents.”

Step‑by‑step guide explaining what this does and how to use it:
This guide demonstrates how to create a simple RAG pipeline that loads a text file, splits it into chunks, embeds it, stores it in Chroma, and then queries it.

Environment Setup: Create a new Python project and install the necessary libraries.
```
pip install langchain langchain-community chromadb langchain-openai
```
Note: You’ll need an OpenAI API key for the embeddings. For a local alternative, you can use `langchain-ollama` with a model like nomic-embed-text.

Load and Split Documents: Load your data and split it into manageable chunks.

from langchain_community.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter</li>
</ol>

loader = TextLoader("your_data.txt")  Replace with your text file
documents = loader.load()

text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = text_splitter.split_documents(documents)

3. Create the Vector Store: Generate embeddings and store them in Chroma.

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(docs, embeddings)

4. Create a Retrieval Chain: Set up a chain that retrieves relevant documents and uses them as context for the LLM.

from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA

llm = ChatOpenAI(model="gpt-4o")
qa_chain = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever())

answer = qa_chain.invoke({"query": "What is the main topic of the document?"})
print(answer['result'])

3. Harnessing Data Extraction and Open LLM Access

Data extraction tools such as FireCrawl and Crawl4AI are crucial for feeding an AI application with high-quality, up-to-date information. However, the API costs for commercial models can be prohibitive. Platforms like Groq and Together AI offer a solution by providing high-speed inference for open-source models at a fraction of the cost, enabling both local development and scalable production deployments. This section explains how to use an open-source model hosted on Groq.

Step‑by‑step guide explaining what this does and how to use it:
This guide shows you how to query a powerful open-source model (like Llama 3) using Groq’s API for fast and cost-effective inference.

Get an API Key: Sign up for an API key on the Groq Cloud Console.

2. Install the Groq Python Library:

pip install groq

3. Create a Python Script to Query the Model:

from groq import Groq

client = Groq(api_key="YOUR_GROQ_API_KEY")

chat_completion = client.chat.completions.create(
messages=[
{
"role": "user",
"content": "Explain the importance of AI evaluation frameworks like Ragas.",
}
],
model="llama3-8b-8192",
)

print(chat_completion.choices[bash].message.content)

4. Using with Extracted Data: Combine this with a data extraction tool. For instance, you can use a `curl` command to fetch and summarize a webpage.

 Extract content (using a simple curl and filtering, but in practice use a tool like FireCrawl)
curl -s https://example.com |  This would be complex. In practice, use FireCrawl's API.

Instead of a raw curl, here’s a conceptual Python snippet showing the workflow:

import requests
from bs4 import BeautifulSoup
from groq import Groq

<ol>
<li>Extract Data
response = requests.get("https://www.bleepingcomputer.com/")
soup = BeautifulSoup(response.content, 'html.parser')
This is a simplification. Real extraction uses tools like FireCrawl.
page_text = soup.get_text()</p></li>
<li><p>Summarize with Groq
client = Groq(api_key="YOUR_GROQ_API_KEY")
response = client.chat.completions.create(
messages=[{"role": "user", "content": f"Summarize this: {page_text[:2000]}..."}],
model="llama3-8b-8192",
)
print(response.choices[bash].message.content)

4. Evaluating AI Applications for Production Readiness

One of the most overlooked yet critical layers of the AI stack is evaluation. Shipping an AI application without robust evaluation is akin to deploying a microservice without unit or integration tests. Tools like Ragas, TruLens, and Giskard provide frameworks to measure performance metrics such as answer relevancy, context precision, and hallucination, giving you confidence before deployment. This ensures your application remains reliable and effective as it scales.

Step‑by‑step guide explaining what this does and how to use it:
This guide demonstrates how to set up a basic evaluation pipeline using the Ragas library to test the performance of a RAG system.

1. Install Ragas:

pip install ragas

2. Prepare your Test Data: You will need a dataset of questions, ground-truth answers, and the retrieved contexts from your RAG pipeline. For simplicity, this example creates a synthetic dataset.

3. Run the Evaluation:

from ragas import evaluate
from ragas.metrics import context_precision, answer_relevancy, faithfulness
from datasets import Dataset

Simulated data from a RAG pipeline
data = {
"question": ["What is the capital of France?", "What is the largest planet?"],
"answer": ["Paris is the capital of France.", "Jupiter is the largest planet in our solar system."],
"contexts": [["The capital of France is Paris."], ["Jupiter is the largest planet."]],
}
dataset = Dataset.from_dict(data)

result = evaluate(dataset, metrics=[context_precision, answer_relevancy, faithfulness])
print(result)

4. Interpreting the Results: The output will provide scores for each metric (usually between 0 and 1). A high `faithfulness` score indicates the answer is grounded in the provided context, while a high `answer_relevancy` score means the answer directly addresses the question. Low scores would prompt a review of your retrieval mechanism or the prompting strategy.

What Undercode Say:

Modularity is Key: The AI stack is not a monolithic platform but a collection of interchangeable components. Understanding how to swap out the LLM, vector database, or evaluation framework is crucial for optimizing performance and cost.
Data is the Foundation: The quality of your data extraction and embedding pipelines is often more important than the choice of the LLM itself. “Garbage in, garbage out” remains a fundamental truth in AI.
Local Development vs. Production: Tools like Ollama enable rapid local prototyping, but platforms like Groq are essential for achieving production-level performance and scale. The modern AI engineer must be fluent in both environments.
The Importance of Observability: Evaluation is not a one-time step but an ongoing process. As your data changes and new models are released, continuous monitoring and evaluation are necessary to maintain application quality and trustworthiness. An AI app is a living system that requires constant performance monitoring, much like a complex distributed system.

Prediction:

+1: The democratization of AI through open-source models and accessible platforms like Ollama and Hugging Face will continue to accelerate, enabling a new wave of innovation from small startups and individual developers.
-1: The increasing complexity of the AI stack will lead to a “tooling fragmentation,” where choosing the wrong combination of technologies (e.g., a misconfigured vector database or an inefficient orchestrator) can result in significant performance bottlenecks and high latency, potentially undermining the user experience.
+1: We will see a rise in specialized, vertically integrated AI stacks that are pre-optimized for specific industries (e.g., healthcare, finance, legal). These will simplify deployment by offering curated combinations of data extraction tools, models, and evaluation suites.
-1: The reliance on complex data pipelines for RAG will make AI applications a prime target for supply chain attacks, where an attacker compromises a data extraction tool or a vector database to inject malicious content.
+1: The evolution of evaluation frameworks like Ragas and Giskard will be key to building trust in AI. As these tools become more sophisticated and integrated into CI/CD pipelines, they will enable more responsible and reliable AI deployment, turning AI from a novelty into a dependable enterprise technology.

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