1GenAI
2├── Text Generation (LLMs)
3│   ├── GPT-4, Claude, Gemini
4│   └── Use cases: Writing, coding, analysis
5├── Image Generation
6│   ├── DALL-E, Midjourney, Stable Diffusion
7│   └── Use cases: Art, design, marketing
8├── Code Generation
9│   ├── GitHub Copilot, Cursor, Codeium
10│   └── Use cases: Programming, debugging
11├── Audio/Music
12│   └── MusicGen, Suno
13└── Video
14    └── Runway, Pika

1Input Text → Tokenization → Embeddings → 
2Multi-Head Attention → Feed Forward → 
3Layer Normalization → Output Probabilities → Generated Text

1[Instruction] + [Context] + [Input Data] + [Output Format]

1Prompt: Phân tích cảm xúc câu sau: "Sản phẩm này thật tuyệt vời!"
2 
3Output: Positive

1Prompt:
2Câu: "Tôi yêu sản phẩm này" → Positive
3Câu: "Chất lượng tệ" → Negative
4Câu: "Giao hàng nhanh, nhưng đóng gói kém" → ?
5 
6Output: Mixed

1Prompt: Hãy giải bài toán sau từng bước:
2Nếu 1 quả táo giá 5k, 3 quả cam giá 12k, 
3mua 2 táo + 4 cam hết bao nhiêu?
4 
5Hãy reasoning step by step.
6 
7Output:
81. Giá 1 táo = 5k → 2 táo = 10k
92. Giá 3 cam = 12k → 1 cam = 4k → 4 cam = 16k
103. Tổng = 10k + 16k = 26k

1# Example: Content writer
2prompt = """
3[Role] Bạn là content writer chuyên nghiệp.
4
5[Context] Đang viết blog post về AI cho startup Việt Nam.
6
7[Limit] Độ dài: 300 từ. Tone: Friendly, professional.
8
9[Example] 
10Heading: "3 cách AI giúp startup tăng năng suất"
11- Tự động hóa customer support
12- Phân tích dữ liệu khách hàng
13- Tạo content marketing
14
15[Adjust] Thêm số liệu cụ thể và case study Việt Nam nếu có.
16"""

1Prompt: Act as a [ROLE] with [EXPERTISE].
2 
3Examples:
4- "Act as a senior data scientist with 10 years experience in healthcare analytics..."
5- "Act as a creative director at a top advertising agency..."
6- "Act as a Python expert specializing in data engineering..."

1Prompt: Trả lời theo format JSON:
2{
3  "summary": "Tóm tắt ngắn gọn",
4  "key_points": ["điểm 1", "điểm 2"],
5  "sentiment": "positive/negative/neutral",
6  "confidence": 0.95
7}

1Analyze the following [TYPE] and provide:
21. Summary (2-3 sentences)
32. Key insights (bullet points)
43. Recommendations (actionable steps)
54. Risks/Concerns (if any)
6 
7[DATA/TEXT]:
8...

1Write a [FORMAT] about [TOPIC] that:
2- Target audience: [WHO]
3- Tone: [TONE]
4- Length: [WORDS]
5- Must include: [REQUIREMENTS]
6- Avoid: [RESTRICTIONS]

1Write [LANGUAGE] code to [TASK].
2 
3Requirements:
4- Use [LIBRARY/FRAMEWORK]
5- Handle [EDGE CASES]
6- Include error handling
7- Add comments explaining logic
8 
9Example input: ...
10Expected output: ...

1# Core
2import streamlit as st
3import openai
4from datetime import datetime
5import json
6
7# Features
8- Streamlit for UI
9- OpenAI API for LLM
10- JSON for conversation storage
11- Environment variables for API keys

1# app.py
2import streamlit as st
3from openai import OpenAI
4
5st.title("🤖 Personal AI Assistant")
6
7# Initialize OpenAI
8client = OpenAI(api_key=st.secrets["OPENAI_API_KEY"])
9
10# Session state for conversation history
11if "messages" not in st.session_state:
12    st.session_state.messages = []
13
14# System prompt selector
15system_role = st.sidebar.selectbox(
16    "Choose AI Role:",
17    ["General Assistant", "Code Expert", "Writing Coach", "Data Analyst"]
18)
19
20system_prompts = {
21    "General Assistant": "You are a helpful AI assistant.",
22    "Code Expert": "You are an expert programmer. Provide clean, efficient code with explanations.",
23    "Writing Coach": "You are a professional writing coach. Help improve writing clarity and style.",
24    "Data Analyst": "You are a data analyst. Provide insights and data-driven recommendations."
25}
26
27# Display chat messages
28for message in st.session_state.messages:
29    with st.chat_message(message["role"]):
30        st.markdown(message["content"])
31
32# Chat input
33if prompt := st.chat_input("What would you like to know?"):
34    # Add user message
35    st.session_state.messages.append({"role": "user", "content": prompt})
36    with st.chat_message("user"):
37        st.markdown(prompt)
38    
39    # Get AI response
40    with st.chat_message("assistant"):
41        messages = [
42            {"role": "system", "content": system_prompts[system_role]},
43            *st.session_state.messages
44        ]
45        
46        stream = client.chat.completions.create(
47            model="gpt-4-turbo-preview",
48            messages=messages,
49            stream=True
50        )
51        
52        response = st.write_stream(stream)
53    
54    st.session_state.messages.append({"role": "assistant", "content": response})
55
56# Sidebar controls
57if st.sidebar.button("Clear Conversation"):
58    st.session_state.messages = []
59    st.rerun()
60
61# Export conversation
62if st.sidebar.button("Export Chat"):
63    chat_export = {
64        "timestamp": datetime.now().isoformat(),
65        "role": system_role,
66        "messages": st.session_state.messages
67    }
68    st.sidebar.download_button(
69        "Download JSON",
70        data=json.dumps(chat_export, indent=2),
71        file_name=f"chat_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
72    )

1# Temperature control
2temperature = st.sidebar.slider("Creativity", 0.0, 2.0, 0.7)
3
4# Token usage tracking
5total_tokens = sum(msg.get('tokens', 0) for msg in st.session_state.messages)
6st.sidebar.metric("Tokens Used", total_tokens)
7
8# Prompt templates
9template = st.sidebar.selectbox("Quick Prompts:", [
10    "Summarize this text",
11    "Explain like I'm 5",
12    "Generate code for",
13    "Translate to Vietnamese"
14])

1# Token estimation
2def estimate_tokens(text):
3    return len(text) // 4  # Rough estimate
4
5# Use cheaper models for simple tasks
6if task_complexity == "simple":
7    model = "gpt-3.5-turbo"
8else:
9    model = "gpt-4-turbo-preview"
10
11# Limit context window
12max_history = 10
13messages = messages[-max_history:]