Generative AI: Large Language
Model
1 What is Generative AI.................................................................................................................. 2
1.1 What Generative AI Can Do:................................................................................................ 2
1.2 Why is it significant?............................................................................................................. 3
2 What is LLM................................................................................................................................ 4
2.1 How LLM Work?.................................................................................................................. 4
2.2 Key features of LLM............................................................................................................. 6
2.3 Application of LLM.............................................................................................................. 7
2.4 Challenges and Limitation.................................................................................................... 7
2.4.1 Hallucination.................................................................................................................. 7
2.4.2 Bias & Fairness.............................................................................................................. 7
2.4.3 Computate & Cost.......................................................................................................... 7
2.4.4 Data Privacy................................................................................................................... 7
2.5 Emerging Trends................................................................................................................... 7
3 Prompt Engineering...................................................................................................................... 8
4 Current most popular LLM.......................................................................................................... 9
5 Can LLM thinking like human's
brain?..................................................................................... 10
6 How to apply LLM to business ?............................................................................................... 11
6.1 Core Business Use Cases..................................................................................................... 11
6.2 By Industry.......................................................................................................................... 12
6.3 How to start?....................................................................................................................... 12
7 Best Practices & Mitigation....................................................................................................... 13
8 Reference.................................................................................................................................... 16
Generative AI is a fascinating and rapidly evolving subfield of
artificial intelligence that focuses on creating new, original content rather
than just analyzing or classifying existing data.
1.1 What Generative AI Can Do:
Generative AI models are capable of producing a
wide range of content, including:
Text: Writing articles, stories, poems, emails, code, scripts,
summaries, and even engaging in human-like conversations (e.g., ChatGPT,
Gemini).
Images: Generating realistic images from text descriptions, modifying
existing images, creating unique artwork, and even designing new products
(e.g., DALL-E, Midjourney, Stable Diffusion).
Audio: Composing music, synthesizing natural-sounding speech for voice
assistants, or generating sound effects.
Video: Creating animations from text prompts or applying special effects
to existing video.
Code: Generating original software code, autocompleting code snippets,
and translating between programming languages.
3D Models and
Simulations: Designing new objects,
environments, or even simulating complex systems like molecular structures for
drug discovery.
Generative AI is a game-changer because it moves
AI beyond analysis and prediction into the realm of creation. This opens up a
vast array of applications across industries, from automating content creation
and boosting creativity to accelerating scientific discovery and improving
customer experiences. It's a powerful tool that can augment human capabilities
and transform how we interact with technology.
1. Input Text: the process starts with raw text data
2. Tokenization: The text is split into smaller units called tokens, which can
be words or subword pieces.
- Why It Matters: LLMs process tokens, not raw text. Tokens can represent
words, subwords, or characters, depending on the
tokenizer.
3. Embedding: Each token is
transformed into a numerical vector that captures semantic properties
- Example: Similar words like "king" and "queen" will have
embeddings close to each other in vector space.
- Why It Matters: Embeddings allow the model
to understand relationships between words.
4. Transformer Layers: Stacked transformer layers, built around self-attention and
feed-forward networks, process these vectors to understand context and
relationships between tokens
- Core Idea: Transformers are the backbone of LLMs. They use mechanisms like
self-attention to process input efficiently.
Key Components:
- Encoder-Decoder: Used in models like BERT
(encoder-only) and GPT (decoder-only).
- Self-Attention: Helps the model focus on
relevant parts of the input.
- Feedforward Layers: Process the attention
outputs to make predictions.
5. Decoding & Text
Generation: Using the learned
patterns, the model generates output text based on various decoding strategies
(e.g., greedy decoding, beam search).
6. Output Text: Finally, the generated tokens are converted back into
human-readable text
Context Awareness
- LLMs can understand the context of a sentence
or paragraph, enabling coherent responses.
Few-Shot and Zero-Shot
Learning
- Few-Shot: The model learns tasks with minimal
examples.
- Zero-Shot: The model performs tasks without
prior examples.
Scalability
- Larger models (e.g., GPT-4) with billions of
parameters perform better but require more computational resources.
- Conversational Agents: Chatbots,
virtual assistants
- Text Generation: Articles, marketing
copy, creative writing
- Code Generation & Completion: From
snippets to full functions
- Question Answering & Summarization:
From documents, customer tickets
- Translation & Paraphrasing:
Crossing language barriers, clarifying meaning
Confidently generating false or nonsensical
facts.
Reflecting or amplifying societal biases in training
data.
2.4.3 Computate & Cost
Billions of parameters demand heavy GPUs,
energy, and budget.
Risk of memorizing and regurgitating sensitive
data.
- Multimodal LLMs: Merging text, vision,
audio (e.g., GPT-4s image understanding).
- Sparse & Mixture-of-Experts:
Activating only parts of the model per input to scale efficiently.
- Continual Learning: Updating models
incrementally without forgetting past knowledge.
- On-Device & Edge LLMs: Tiny LLMs
optimized for smartphones and embedded devices.
|
Category |
Tips |
Examples |
|
Clear Objective |
State your goal
explicitly |
Summarize this
article in 3 bullet points. |
|
Prompt Structure |
Use system/user roles;
separate context |
System: You are an
expert historian. User: Describe WWII. |
|
Context Provision |
Give relevant
background or data |
Here is sales data:
[table]. Analyze Q1 vs. Q2 trends. |
|
Formatting Guides |
Specify output format |
Return JSON with
title and summary fields. |
|
Temperature |
Control creativity
(01 scale) |
Use temperature=0.2
for factual output. |
|
Max Tokens |
Limit length of
response |
Limit to 150 tokens. |
|
Few-Shot Prompting |
Provide examples for
format consistency |
Q: Whats the capital
of France? A: Paris. Q: Spain? A: |
|
Chain-of-Thought |
Encourage stepwise
reasoning |
Think step by step to
solve this math problem. |
|
Role Play |
Assign persona to influence
style |
Youre a marketing
guru. Draft an email campaign. |
|
Error Handling |
Ask for self-checks or
clarifications |
If any data is
missing, ask for more info before proceeding. |
|
Follow-Up Prompts |
Plan for iterative
refinement |
Now expand on point 2
with more technical details. |
|
Common Pitfalls |
Avoid ambiguity; watch
pronoun references |
Dont use it
without clear antecedent. |
Heres a side-by-side comparison of todays leading LLMs based on
Artificial Analysis benchmarks:
|
Model |
Provider |
Context Window |
Intelligence Index |
Price (USD/1M tokens) |
Output Speed (tokens/s) |
Latency to 1st Chunk (s) |
|
o3-pro |
OpenAI |
200 K |
71 |
$35.00 |
23.3 |
106.9 |
|
Gemini 2.5 Pro |
Google DeepMind |
1 M |
70 |
$3.44 |
147.9 |
34.7 |
|
o3 |
OpenAI |
128 K |
70 |
$3.50 |
140.1 |
16.9 |
|
o4-mini (high) |
OpenAI |
200 K |
70 |
$1.93 |
161.6 |
36.4 |
|
DeepSeek R1 0528 |
DeepSeek AI |
128 K |
68 |
$0.96 |
29.4 |
2.4 |
Key insights:
·
Highest IQ: o3-pro (71)
·
Biggest context: Gemini 2.5 Pro (1 M
tokens)
·
Best throughput: o4-mini (161.6 t/s)
·
Lowest latency: DeepSeek R1 (2.4 s)
·
Cheapest at scale: DeepSeek R1 and
o4-mini
Beyond these headline
metrics, you might weigh:
·
Agentic tool-use (e.g.
web browsing, code exec)
·
Multimodal handling
(vision + language)
·
Fine-tuning &
embedding support
5 Can LLM thinking like human's
brain?
LLMs do not actually think like a human brain.
✅ How LLMs work:
LLMs are trained to predict the most likely next word in a
sequence of text, based on patterns in huge amounts of data.
They use mathematical transformations (like attention mechanisms
in transformers) to create representations of text.
They dont have consciousness, emotions, or real understanding of
the world.
✅ How human brains work:
Your brain combines memory, perception, emotion, intuition, and
reasoning.
You have self-awareness and an internal subjective experience
(what philosophers call qualia).
You can form intentions, set goals, and interpret meaning beyond
patterns.
✅ What LLMs can do that resembles thinking:
They can generate answers, summarize, reason through logical steps
(to an extent), and simulate conversation.
This mimics some aspects of language-based reasoning.
But its fundamentally pattern recognition, not true
comprehension.
In short:
LLMs simulate aspects of human-like language output but they dont
think or understand the way your brain does. They are sophisticated statistical
tools, not minds.
6 How to apply LLM to business ?
1. Customer Support
*
Chatbots that handle FAQs, complaints, and returns.
*
Auto-generate personalized email responses.
*
Tools: GPT-based assistants, Zendesk integrations.
2. Marketing & Sales
* Write
ads, product descriptions, landing pages.
*
Generate email campaigns tailored to segments.
*
Analyze customer sentiment from social media.
3. Operations & Productivity
*
Automate document generation (contracts, reports, invoices).
*
Summarize meetings, extract action items (e.g. Zoom + AI tools).
*
Translate or localize content instantly.
4. Data & Analytics
*
Natural language interfaces to databases (Ask your data tools).
*
Generate insights from reports or dashboards.
*
Simplify analytics for non-technical users.
5. Product Development
*
Brainstorm feature ideas or user stories.
*
Auto-generate code snippets or documentation.
*
AI-powered prototyping or UX writing.
E-commerce: Personalized recommendations, automated product tagging, dynamic
customer service.
Finance: AI assistants for client communication, report generation, fraud
pattern explanation.
Healthcare: Auto-generate patient summaries, triage chatbot, simplify medical
notes.
Legal: Draft contracts, summarize legal documents, compare regulations.
Real Estate: Auto-write listings, chatbot for property Q\&A, summarize
offers.
1. Pick one workflow thats time-consuming and
text-heavy.
2. Use tools like ChatGPT, Claude, or
open-source LLMs (e.g., LLaMA 3, Mistral).
3. Integrate via APIs or no-code platforms
(Zapier, Make, LangChain).
4. Test, fine-tune, and monitor for accuracy.
5. Scale to other departments.
1. Prompt Engineering & Input Control
Frame questions clearly and consistently (e.g., label roles:
User: / Assistant:).
Use few-shot or chain-of-thought exemplars to guide style and
reasoning.
Validate and sanitize user inputs to prevent injection or
adversarial prompts.
Lock down system prompts and enforce token-level policies on
forbidden content.
2. Retrieval-Augmented Generation (RAG)
Ground answers in trusted knowledge bases (databases, vector
stores).
Design hits-based retrieval: fetch top-k relevant docs, then let
LLM refine.
Track source citations in outputs to boost transparency.
Periodically refresh your index to avoid stale or outdated info.
3. Fine-Tuning & Parameter-Efficient Tuning
Fully fine-tune only when you have abundant, high-quality
labeled data.
Leverage adapters, LoRA, or prefix
tuning for domain-specific tweaks with minimal compute.
Split data into train/validation/test to catch overfitting and
data leakage.
Continuously evaluate on edge cases and rare scenarios.
4. Reinforcement Learning from Human Feedback (RLHF)
Collect diverse human rankings on model outputs to train a
reward model.
Use robust RL algorithms (e.g., PPO) with clipped updates to
prevent mode collapse.
Rotate human annotators and audit reward signals for bias drift.
Regularly re-calibrate with fresh feedback as your domain
evolves.
5. Bias & Fairness Mitigations
Assess dataset demographics and perform subgroup performance
analysis.
Apply data augmentation or re-sampling to balance
underrepresented groups.
Use bias-detection tools (e.g., What-If Toolkit) and adversarial
bias tests.
Deploy post-hoc debiasing: counterfactual data augmentation and
output filters.
6. Hallucination Detection & Grounding
Integrate secondary verification: query external APIs or
fact-checkers.
Set up confidence thresholds; route low-confidence queries to
safe fallbacks.
Train a hallucination classifier on synthetic made-up vs.
grounded pairs.
Log hallucination incidents and retrain/fine-tune to plug
knowledge gaps.
7. Output Moderation & Safety Filters
Implement multi-stage filters: regex-based checks + ML
classifiers for toxicity, hate, PII.
Throttle or quarantine outputs flagged as high-risk before user
delivery.
Maintain an allow-list vs. block-list for sensitive topics
(medical, legal, financial).
Continuously expand your filter taxonomy as new threat vectors
emerge.
8. Data Privacy & PII Handling
Enforce input redaction: strip or hash personal identifiers
before inference.
Avoid logging raw user content; store only anonymized embeddings
or metadata.
Use on-device or private-cloud inference for regulated or
sensitive domains.
Regularly audit data retention and compliance with GDPR, CCPA,
HIPAA, etc.
9. Security & Access Control
Authenticate and authorize all model calls via API keys or OAuth
scopes.
Isolate high-privilege models in segmented VPCs with strict
ingress/egress rules.
Monitor for abnormal usage patterns (spikes, unusual queries)
and auto-throttle.
Pen-test your prompt-to-API pipeline for injection attacks and
privilege escalation.
10. Monitoring, Logging & Metrics
Track quality metrics: perplexity, BLEU/CIDEr
for generation tasks.
Instrument production with latency, error rates, token
consumption dashboards.
Set up alerts on drift: sudden drops in accuracy or spikes in
hallucinations.
Use A/B testing to compare model versions under real-world load.
11. Model Efficiency & Deployment
Apply model distillation or quantization to shrink footprint and
reduce cost.
Leverage sparse or MoE architectures
to activate only needed parameters.
Cache frequent prompts and outputs to save compute cycles.
Use auto-scaling inference clusters to match user demand.
12. Explainability & Transparency
Provide users with traceable rationale sections or citation
links.
Implement attention- or gradient-based saliency maps for
internal audits.
Offer users logs of what data the model accessed (in RAG
setups).
Publish model cards detailing training data, architecture, known
limitations.
https://medium.com/@lmpo/tokenization-and-word-embeddings-the-building-blocks-of-advanced-nlp-c203b78bfd07
https://medium.com/@asadali.syne/understanding-the-transformer-architecture-in-llm-e475453879fe