Part 1: AI Terms Explained Simply and how AI can help you at work. Understand the meaning behind Token,Hallucination, Context Window etc.

If you found yourself wondering about the AI terms what do they mean then this is the must read for you

The mind map was generated using NotebookLM

I have been using AI extensively since ChatGPT revolution happened. I have been fascinated with the tools and workings of AI. If you have heard some terms in AI that may have not made sense of it and have not searched and learned about it, then this is the read for you.

I will also highlight how can you use it to add AI to your workflow. I have used us for all these use case along with understanding and writing the code with tools like Copilot.

Essential Terms Everyone Should Know

Artificial Intelligence (AI)

AI, in simple terms, is a simulation of human intelligence in machines that are programmed to think and learn. These machines can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

AI systems use algorithms and models to analyze data, learn from it, and make predictions or decisions. Machine Learning (ML) and Deep Learning (DL) are subsets of AI that focus on the development of algorithms and neural networks to enable computers to learn from and make decisions based on data. We will cover these terms later in this post.

Machine Learning (ML)

A subset of AI where systems improve through experience with data rather than explicit programming.

How it works in simple terms: Instead of writing specific rules, developers feed examples to the computer, which learns patterns and applies them to new situations.

Real-world examples:

• Spam filters that improve as they see more examples of spam

• (Amazon)Product recommendation systems that learn your preferences over time

• Predictive text on your phone that adapts to your writing style Key distinction: Traditional software follows fixed rules (if X, then Y); ML systems develop their own rules based on examples.

Neural Networks

What they actually are: Computing systems loosely inspired by the human brain's structure and try to mimic neurons.

They are Interconnected layers of digital "neurons" that process information by passing it through multiple stages.

Deep Learning

A subset of ML using neural networks with many layers (hence "deep").

Real-world applications:

• Image recognition in medical scans

• Language translation services

• Voice recognition systems

Why it's revolutionary: Deep learning has enabled AI capabilities that were previously impossible, like generating realistic images or writing coherent paragraphs.

Common AI Terms you may have come across

Prompt

The input you provide to an AI system—your question, instruction, or request.

Why it matters: The quality and specificity of your prompt greatly affects the output you receive.

Examples of effective prompts:

• "Draft a customer email explaining our new return policy, emphasizing our 30-day guarantee, using a friendly but professional tone."

• "Analyze these quarterly sales figures and identify the top 3 performing products and any concerning trends."

Prompt engineering: The practice of crafting inputs to get the best possible AI outputs—becoming an increasingly valuable workplace skill.

I have recently written an article about Prompt Engineering

Training Data

What it actually is: The information AI systems learn from during development.

Why it matters at work:

• Affects what the AI knows (and doesn't know)

• Influences potential biases in AI outputs

• Determines the AI's knowledge cutoff date

Practical implications:

• AI won't know about events after its training cutoff

• May have limitations in specialized domains not well-represented in training

• Could reflect societal biases present in its training materials

Generative AI

What it actually is: AI systems that create new content rather than just analyzing existing information.

Popular examples:

• Text: ChatGPT, Claude, Bard

• Images: ChatGPT, Gemini,Grok

• Audio: Descript, ElevenLabs

Business applications:

• Creating customized content for different customer segments

• Generating variations of marketing materials for testing

• Producing drafts of routine business communications

• Creating mockups and prototypes rapidly

Ethical considerations: Issues around copyright, originality, and proper attribution are still evolving.

Large Language Models (LLMs)

What they actually are: AI systems trained on vast amounts of text that can understand and generate human language.

Examples: GPT-4, Claude, LLaMA, Gemini

Why they're transforming work: They can understand context, follow instructions, and generate human-quality text across countless domains.

Common misconception: Despite seeming intelligent, they don't truly "understand" text—they predict likely word sequences based on patterns in training data.

How AI Actually Works in Simple Terms

Pattern Recognition

What it actually is: AI identifying regularities and relationships in data. Everyday example: How your music streaming service figures out what songs to recommend based on what you've listened to before.

Business applications:

• Identifying which customer behaviors predict churn

• Detecting unusual patterns that might indicate fraud

• Recognizing common themes in customer feedback

Key insight: Most AI success comes from discovering patterns too subtle or complex for humans to notice manually.

Prediction

What it actually is: Using historical data to forecast future outcomes or behaviors. Business examples:

• Forecasting inventory needs based on seasonal patterns

• Predicting which marketing leads are most likely to convert

• Estimating project completion times based on similar past projects Practical applications: Enables more accurate planning, resource allocation, and risk management.

Classification

What it actually is: Sorting items into predefined categories.

• Routing customer service inquiries to appropriate departments

• Categorizing expenses for accounting purposes

• Flagging potentially problematic content in social media management

Natural Language Processing (NLP)

What it actually is: AI's ability to work with human language in useful ways.

• Extracting key information from unstructured documents

• Analyzing sentiment in customer feedback

• Translating content between languages

• Summarizing long documents into key points

Common AI Limitations to Be Aware Of

Knowledge Cutoffs

What they actually are: The point after which AI systems have no training data.

Practical implications:

• AI won't know about events, products, or regulations after its cutoff

• May give outdated information about rapidly changing topics

• Unable to access real-time data without special integrations

How to handle it: Always verify time-sensitive information and provide recent context when needed.

Hallucinations

What they actually are: When AI confidently presents incorrect information as fact.

Why they happen: AI systems generate text based on statistical patterns, not genuine understanding, so they can produce plausible-sounding but false information.

Common examples:

• Inventing non-existent sources, studies, or statistics

• Creating made-up product details or company policies

• Fabricating historical events or technical specifications

Risk management strategies:

• Always verify factual claims, especially statistics and references

• Be especially careful with niche topics where training data may be limited

• Use AI for creative ideation but not as the final authority on facts

Reasoning Limitations

What they actually are: Gaps in AI's ability to apply logic or common sense. Examples:

• Difficulty with complex math or multi-step reasoning

• Struggles with understanding physical causality

• Problems with temporal reasoning (understanding time sequences)

Workplace implications: AI tools may need human supervision for tasks requiring deep reasoning or judgment.

Context Windows

What they actually are: Limits on how much information AI can consider at once.

Practical impact:

• Long documents may need to be processed in chunks

• AI might forget details mentioned earlier in very long conversations

Complex projects might require breaking down into smaller components

Workaround: Summarize key points when continuing long interactions or processing large documents.

AI Tools You'll Actually Use at Work

Conversational AI & Assistants

What they do: Engage in dialogue to answer questions and perform tasks.

Common examples: Microsoft Copilot ,ChatGPT, Claude or Your own company LLM

Content Generation Tools

What they do: Create various forms of content based on prompts or specifications.

• Text generators: Create written content from Product Guides, Research on topics and summarize them

• Image generators: Create visual content based on text descriptions

• Code generators: Write or complete programming code (GitHub Copilot,Cursor,Lovable,Bolt)

I have written about AI app generator tools which does not require coding knowledge and you can talk to them in plan english.

Data Analysis AI Tools

What they do: Help extract insights from data without requiring advanced technical skills.

Common examples: Microsoft Power BI with AI features, Tableau with Ask Data, Google Analytics with insights

How they're changing work:

• Allowing non-technical staff to query data using natural language

• Automatically identifying trends and anomalies in business data

• Creating visualizations and reports with minimal manual effort

• Forecasting future trends based on historical data

Practical applications:

• Asking "Show me sales trends by region for Q1" instead of building a complex query

• Automatically identifying unusual patterns in expense reports

• Getting plain-language explanations of what's driving changes in metrics

Practical AI Skills for Regular Work

Effective Prompting

I have covered Prompt Engineering in my latest newsletter.

What it actually is: The skill of giving clear instructions to AI tools to get useful results.

Key principles:

1. Be specific: Include details about format, length, tone, and audience

2. Provide context: Give background information relevant to your request

3. Use examples: Show the AI what good outputs look like

4. Break down complex tasks: Split big requests into manageable steps

Examples of weak vs. strong prompts:

• Weak: "Write social media content for our product."

• Strong: "Write three LinkedIn posts (150 words each) promoting our new project management software. Emphasize time-saving features and integration capabilities. Target audience: IT managers at mid-sized companies. Include relevant hashtags."

Result Evaluation

What it actually is: Critically assessing AI outputs for accuracy, relevance, and appropriateness.

Essential checks:

• Factual accuracy: Verify specific claims, statistics, and references

• Completeness: Ensure all requested elements are included

• Coherence: Check that the content makes logical sense

• Appropriateness: Confirm tone and content match your needs

• Originality: Check for potential plagiarism when originality matters

Red flags to watch for:

• Inconsistencies within the content

• Overly generic statements lacking specificity

• "Hallucinated" information (plausible but made-up facts)

• Outdated information presented as current

AI Collaboration

What it actually is: Working with AI as a partner rather than just a tool. Effective approaches:

• Use AI for first drafts, then refine with your expertise

• Have AI review and suggest improvements to your work

• Iterate through multiple rounds of feedback and refinement

• Combine outputs from multiple prompts into a cohesive whole

Example workflow:

1. Ask AI to generate outline or first draft

2. Review and identify areas for improvement

3. Request specific revisions or expansions

4. Add your unique insights and expertise

5. Ask AI to proofread and suggest final enhancements

Let me know what you would like to learn about in the next post