Artificial Intelligence & ChatGPT Prompts
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๐Ÿ”“Unlock Your Coding Potential with ChatGPT
๐Ÿš€ Your Ultimate Guide to Ace Coding Interviews!
๐Ÿ’ป Coding tips, practice questions, and expert advice to land your dream tech job.


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๐Ÿš€ ๐Ÿณ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ + ๐—Ÿ๐—ถ๐—ป๐—ธ๐—ฒ๐—ฑ๐—œ๐—ป ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜๐—ผ ๐—•๐—ผ๐—ผ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐Ÿ˜

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๐—™๐—ฅ๐—˜๐—˜ ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ๐—ฐ๐—น๐—ฎ๐˜€๐˜€ ๐—ง๐—ผ ๐—š๐—ฒ๐˜ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—๐—ผ๐—ฏ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐Ÿ˜

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Here are some essential SQL tips for beginners ๐Ÿ‘‡๐Ÿ‘‡

โ—† Primary Key = Unique Key + Not Null constraint
โ—† To perform case insensitive search use UPPER() function ex. UPPER(customer_name) LIKE โ€˜A%Aโ€™
โ—† LIKE operator is for string data type
โ—† COUNT(*), COUNT(1), COUNT(0) all are same
โ—† All aggregate functions ignore the NULL values
โ—† Aggregate functions MIN, MAX, SUM, AVG, COUNT are for int data type whereas STRING_AGG is for string data type
โ—† For row level filtration use WHERE and aggregate level filtration use HAVING
โ—† UNION ALL will include duplicates where as UNION excludes duplicates 
โ—† If the results will not have any duplicates, use UNION ALL instead of UNION
โ—† We have to alias the subquery if we are using the columns in the outer select query
โ—† Subqueries can be used as output with NOT IN condition.
โ—† CTEs look better than subqueries. Performance wise both are same.
โ—† When joining two tables , if one table has only one value then we can use 1=1 as a condition to join the tables. This will be considered as CROSS JOIN.
โ—† Window functions work at ROW level.
โ—† The difference between RANK() and DENSE_RANK() is that RANK() skips the rank if the values are the same.
โ—† EXISTS works on true/false conditions. If the query returns at least one value, the condition is TRUE. All the records corresponding to the conditions are returned.

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๐—ช๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฆ๐—ธ๐—ถ๐—น๐—น๐˜€ ๐—ง๐—ต๐—ฎ๐˜ ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€ ๐—”๐—ฟ๐—ฒ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐—™๐—ผ๐—ฟ?๐Ÿ˜

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Guide to Building an AI Agent

1๏ธโƒฃ ๐—–๐—ต๐—ผ๐—ผ๐˜€๐—ฒ ๐˜๐—ต๐—ฒ ๐—ฅ๐—ถ๐—ด๐—ต๐˜ ๐—Ÿ๐—Ÿ๐— 
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses

๐Ÿ“Œ Tip: Experiment with models & fine-tune prompts to enhance reasoning.

2๏ธโƒฃ ๐——๐—ฒ๐—ณ๐—ถ๐—ป๐—ฒ ๐˜๐—ต๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜โ€™๐˜€ ๐—–๐—ผ๐—ป๐˜๐—ฟ๐—ผ๐—น ๐—Ÿ๐—ผ๐—ด๐—ถ๐—ฐ
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.

๐Ÿ“Œ Choosing the right approach improves reasoning & reliability.

3๏ธโƒฃ ๐——๐—ฒ๐—ณ๐—ถ๐—ป๐—ฒ ๐—–๐—ผ๐—ฟ๐—ฒ ๐—œ๐—ป๐˜€๐˜๐—ฟ๐˜‚๐—ฐ๐˜๐—ถ๐—ผ๐—ป๐˜€ & ๐—™๐—ฒ๐—ฎ๐˜๐˜‚๐—ฟ๐—ฒ๐˜€
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?

๐Ÿ“Œ Clear system prompts shape agent behavior.

4๏ธโƒฃ ๐—œ๐—บ๐—ฝ๐—น๐—ฒ๐—บ๐—ฒ๐—ป๐˜ ๐—ฎ ๐— ๐—ฒ๐—บ๐—ผ๐—ฟ๐˜† ๐—ฆ๐˜๐—ฟ๐—ฎ๐˜๐—ฒ๐—ด๐˜†
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.

๐Ÿ“Œ Example: A financial AI recalls risk tolerance from past chats.

5๏ธโƒฃ ๐—˜๐—พ๐˜‚๐—ถ๐—ฝ ๐˜๐—ต๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐˜„๐—ถ๐˜๐—ต ๐—ง๐—ผ๐—ผ๐—น๐˜€ & ๐—”๐—ฃ๐—œ๐˜€
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?

๐Ÿ“Œ Example: A support AI retrieves order details via CRM API.

6๏ธโƒฃ ๐——๐—ฒ๐—ณ๐—ถ๐—ป๐—ฒ ๐˜๐—ต๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜โ€™๐˜€ ๐—ฅ๐—ผ๐—น๐—ฒ & ๐—ž๐—ฒ๐˜† ๐—ง๐—ฎ๐˜€๐—ธ๐˜€
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I donโ€™t offer legal advice.")

๐Ÿ“Œ Example: A financial AI focuses on finance, not general knowledge.

7๏ธโƒฃ ๐—›๐—ฎ๐—ป๐—ฑ๐—น๐—ถ๐—ป๐—ด ๐—ฅ๐—ฎ๐˜„ ๐—Ÿ๐—Ÿ๐—  ๐—ข๐˜‚๐˜๐—ฝ๐˜‚๐˜๐˜€
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution

๐Ÿ“Œ Example: A financial AI converts extracted data into JSON.

8๏ธโƒฃ ๐—ฆ๐—ฐ๐—ฎ๐—น๐—ถ๐—ป๐—ด ๐˜๐—ผ ๐— ๐˜‚๐—น๐˜๐—ถ-๐—”๐—ด๐—ฒ๐—ป๐˜ ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ๐˜€ (๐—”๐—ฑ๐˜ƒ๐—ฎ๐—ป๐—ฐ๐—ฒ๐—ฑ)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?

๐Ÿ“Œ Example:
1๏ธโƒฃ One agent fetches data
2๏ธโƒฃ Another summarizes
3๏ธโƒฃ A third generates a report

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โค1
๐Ÿฑ ๐—ฅ๐—ฒ๐—ฎ๐—น-๐—ช๐—ผ๐—ฟ๐—น๐—ฑ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐˜€ ๐˜๐—ผ ๐—•๐˜‚๐—ถ๐—น๐—ฑ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฅ๐—ฒ๐˜€๐˜‚๐—บ๐—ฒ โ€“ ๐—ช๐—ถ๐˜๐—ต ๐—™๐˜‚๐—น๐—น ๐—ง๐˜‚๐˜๐—ผ๐—ฟ๐—ถ๐—ฎ๐—น๐˜€!๐Ÿ˜

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PYTHON SYNTAX
โค1
๐—š๐—ฒ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฟ๐—ฒ๐—ฎ๐—บ ๐—œ๐—ง ๐—๐—ผ๐—ฏ ๐—œ๐—ป ๐—ง๐—ผ๐—ฝ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€๐Ÿ˜

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โญ Beginner To Advanced FULLSTACK
โค4
๐—ฆ๐˜๐—ถ๐—น๐—น ๐—™๐—ฎ๐—ถ๐—น๐—ถ๐—ป๐—ด ๐—ง๐—ฒ๐—ฐ๐—ต ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฒ๐˜„๐˜€? ๐—ง๐—ต๐—ฒ๐˜€๐—ฒ ๐Ÿฏ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐—–๐—ผ๐˜‚๐—น๐—ฑ ๐—™๐—ถ๐—ป๐—ฎ๐—น๐—น๐˜† ๐—–๐—ต๐—ฎ๐—ป๐—ด๐—ฒ ๐—ง๐—ต๐—ฎ๐˜๐Ÿ˜

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โค1