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Introduction:
Artificial Intelligence terminology has rapidly evolved from academic jargon to boardroom currency, yet most professionals nod along to terms like “RAG” and “hallucination” without truly grasping their technical implications. These 12 concepts—curated from Nicolò Bertaia’s viral LinkedIn cheat sheet—aren’t just vocabulary; they represent the foundational building blocks of how modern AI systems operate, fail, and scale within enterprise environments.
Learning Objectives:
- Master the technical definitions and practical applications of 12 core AI/LLM terminology
- Implement mitigation strategies for common AI failure modes including hallucination and token cost explosion
- Deploy production-ready RAG pipelines, fine-tuning workflows, and coding agent integrations across Linux and Windows environments
You Should Know:
- LLMs & Tokenization: The Engine Behind Every Chatbot
Large Language Models (LLMs) like GPT-4, Claude, and Gemini are neural networks trained on internet-scale text corpora to predict the next token in a sequence. Tokens are the atomic units these systems process—a token can be a word fragment, a full word, or even a punctuation mark. Understanding tokenization is critical because every API call is priced per token, and context windows are measured in tokens.
Step-by-Step: Counting Tokens Like a Pro
Linux/macOS (using tiktoken):
Install OpenAI's tokenizer
pip install tiktoken
Count tokens in a file
python3 -c "import tiktoken; enc = tiktoken.encoding_for_model('gpt-4');
print(len(enc.encode(open('prompt.txt').read())))"
Windows (PowerShell):
Using Python from PowerShell
python -c "import tiktoken; enc = tiktoken.encoding_for_model('gpt-4'); print(len(enc.encode(open('prompt.txt').read())))"
Pro Tip: Long prompts and documents increase costs exponentially—always truncate or summarize before sending to the API.
- Hallucination: When AI Sounds Confident but Is Wrong
Hallucination occurs when an LLM generates plausible-sounding but factually incorrect information. This isn’t a bug—it’s a feature of probabilistic generation. The model doesn’t “know” facts; it predicts likely sequences based on patterns. In legal, finance, and customer-facing work, hallucinations can be catastrophic.
Step-by-Step: Building a Hallucination Detection Layer
- Implement source grounding – Always retrieve source documents alongside the answer.
- Use confidence scoring – Many LLM APIs return log probabilities; monitor them.
- Cross-validate with embeddings – Compare the semantic similarity between the answer and source text.
Python snippet for semantic validation:
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer('all-MiniLM-L6-v2')
answer_emb = model.encode(answer)
source_emb = model.encode(source_text)
similarity = util.cos_sim(answer_emb, source_emb)
if similarity < 0.7:
print("⚠️ Potential hallucination detected!")
- Training vs Inference: The Factory vs The Product
Training is the resource-intensive phase where the model learns from billions of documents—this happens in data centers over weeks or months. Inference is when the trained model generates responses to your prompts. Training is the factory; inference is the product being used inside your business.
Step-by-Step: Measuring Inference Latency
Linux:
Time an API call
time curl -X POST https://api.openai.com/v1/chat/completions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"gpt-4","messages":[{"role":"user","content":"Hello"}]}'
Windows (using PowerShell):
Measure-Command {
Invoke-RestMethod -Uri "https://api.openai.com/v1/chat/completions" `
-Method Post `
-Headers @{"Authorization"="Bearer $env:OPENAI_API_KEY"} `
-Body '{"model":"gpt-4","messages":[{"role":"user","content":"Hello"}]}'
}
4. Fine-Tuning: Adapting General Models to Your Domain
Fine-tuning takes a pre-trained model and continues training it on your specific dataset. This is ideal when the same task repeats frequently—support ticket classification, risk assessment, or document summarization. Fine-tuning improves accuracy and reduces prompt size (saving tokens long-term).
Step-by-Step: Fine-Tuning OpenAI Models
1. Prepare your data in JSONL format:
{"messages": [{"role": "system", "content": "You are a financial risk analyst."}, {"role": "user", "content": "Evaluate this transaction..."}, {"role": "assistant", "content": "Risk level: HIGH"}]}
2. Upload and create a fine-tuning job:
Upload file openai api files.create -f training_data.jsonl -p fine-tune Start fine-tuning openai api fine_tunes.create -t file-XXXXX -m gpt-3.5-turbo
3. Monitor progress:
openai api fine_tunes.follow -i ft-XXXXX
- RAG (Retrieval-Augmented Generation): Your Documents Are the New Training Data
RAG is the practice of retrieving relevant documents from your own knowledge base and injecting them into the prompt before the model generates an answer. This is how you keep answers current, factual, and grounded in your proprietary data—without retraining the model.
Step-by-Step: Building a Local RAG Pipeline with ChromaDB
import chromadb
from chromadb.utils import embedding_functions
Initialize ChromaDB
client = chromadb.PersistentClient(path="./rag_db")
collection = client.get_or_create_collection(
name="docs",
embedding_function=embedding_functions.OpenAIEmbeddingFunction()
)
Add documents
collection.add(
documents=["Your internal policy document text here..."],
ids=["doc1"]
)
Query
results = collection.query(query_texts=["What is our policy on X?"], n_results=3)
context = "\n".join(results['documents'][bash])
Send to LLM with context
prompt = f"Context: {context}\n\nQuestion: What is our policy on X?"
Critical: Clean docs, consistent naming, folder structures, and access rules matter enormously. A messy knowledge base yields garbage RAG outputs.
- Chain of Thought: Breaking Hard Problems into Steps
Chain of Thought (CoT) prompting forces the model to reason step-by-step rather than jumping to a conclusion. This dramatically improves accuracy on complex tasks like math, planning, and risk analysis.
Step-by-Step: CoT Prompting Template
Problem: [Your complex question] Let's approach this step by step: Step 1: [Break down the problem] Step 2: [Analyze each component] Step 3: [Consider edge cases] Step 4: [Synthesize the answer] Final answer:
Example for cybersecurity:
“Step 1: Identify all open ports. Step 2: Check for known vulnerabilities on each. Step 3: Prioritize based on exploitability. Step 4: Recommend remediation.”
7. Coding Agents: From Suggestion to Execution
Coding agents like Claude Code and Cursor are AI systems that write, test, and debug code autonomously. They’re moving from “suggestion” to “execution”—meaning they can now run commands, edit files, and execute tests without human intervention.
Step-by-Step: Setting Up a Secure Coding Agent Environment
1. Create an isolated sandbox (Docker):
docker run -it --rm --1ame code-agent \ -v $(pwd)/workspace:/workspace \ python:3.11-slim bash
- Install the agent tool (example with Cursor CLI):
Linux/macOS curl -fsSL https://cursor.sh/install.sh | bash Windows (WSL2 recommended) wsl --install -d Ubuntu
-
Set strict permissions – never run coding agents with production credentials. Use read-only API keys and limit file system access to a specific directory.
-
Implement a human-in-the-loop for code review before any commit.
What Undercode Say:
- AI terms are not trivia – they directly influence tool procurement, vendor evaluation, risk communication, and internal training strategies.
- Pick 3 terms from this list and learn them properly this week – depth beats breadth when it comes to building technical credibility in AI discussions.
- The real value is in asking better questions – when you understand tokenization, you ask about pricing models; when you understand RAG, you ask about data hygiene; when you understand hallucination, you demand source citations.
Analysis: Nicolò Bertaia’s cheat sheet resonates because it bridges the gap between abstract AI research and practical business realities. The 12 terms aren’t randomly selected—they form a mental model for the entire AI lifecycle: how models are built (training, weights, validation loss), how they’re customized (fine-tuning, distillation), how they’re deployed (inference, RAG), and how they fail (hallucination). The inclusion of “coding agents” signals a critical shift: AI is no longer just about generating text—it’s about taking actions. Professionals who internalize these concepts will lead AI adoption conversations; those who don’t will be left nodding along without understanding the risks and costs embedded in every decision.
Prediction:
+1 Enterprise adoption of RAG will triple within 18 months, driving massive demand for data engineers who understand embedding pipelines and vector databases—not just prompt engineers.
+1 Fine-tuning will become commoditized through open-source tooling (e.g., Axolotl, Unsloth), enabling small teams to achieve GPT-4-level performance on domain-specific tasks at 10% of the cost.
-1 Hallucination will cause at least one major regulatory fine ($50M+) for a financial institution that deployed an ungrounded LLM in customer-facing advisory roles without proper validation layers.
+1 Coding agents will automate 40% of routine software maintenance tasks by 2027, shifting developer roles from writing code to reviewing and orchestrating AI-generated contributions.
-1 Token cost explosion will catch unprepared organizations off guard—teams that don’t monitor token usage will face API bills 5x higher than projected within the first quarter of production deployment.
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