Linkedin - Prompt Engineering with LangChain
| 文件类型 | 收录时间 | 最后活跃 | 资源热度 | 文件大小 | 文件数量 |
|---|---|---|---|---|---|
| 视频 | 2025-4-26 14:39 | 2025-5-2 05:35 | 8 | 1.3 GB | 47 |
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文件列表
- 1. Introduction/1. Create powerful LLM driven applications.mp411.68MB
- 2. Introduction to Language Models/1. What are language models.mp49.09MB
- 3. LLMs and Text Generation/1. How do language models generate text.mp412.5MB
- 3. LLMs and Text Generation/2. Base LLMs vs. instruction-tuned LLMs.mp434.96MB
- 3. LLMs and Text Generation/3. Training fine-tuning and in-context learning.mp410.64MB
- 3. LLMs and Text Generation/4. Prompt engineering.mp411.95MB
- 4. Components of LangChain/1. What is LangChain.mp49.57MB
- 4. Components of LangChain/2. LangChain overview.mp48.81MB
- 4. Components of LangChain/3. Model I O- Interface with language models.mp485.23MB
- 4. Components of LangChain/4. Retrieval- Interface with application-specific data.mp458.42MB
- 4. Components of LangChain/5. Chains- Construct sequences of calls.mp474.79MB
- 4. Components of LangChain/6. Agents- Let chains choose tools based on high-level directives.mp455.39MB
- 4. Components of LangChain/7. Memory- Persist application state between runs of a chain.mp440.31MB
- 5. Basics of Prompting/1. Prompt basics.mp44.19MB
- 5. Basics of Prompting/2. Principles and tactics for prompting.mp430.73MB
- 6. Prompt Templates Deep Dive/1. Introduction to prompt templates.mp426.6MB
- 6. Prompt Templates Deep Dive/10. Few-shot prompt templates.mp433.92MB
- 6. Prompt Templates Deep Dive/11. Few-shot prompt templates for chat.mp422.25MB
- 6. Prompt Templates Deep Dive/12. Introduction to example selectors.mp410.73MB
- 6. Prompt Templates Deep Dive/13. Length-based example selector.mp411.38MB
- 6. Prompt Templates Deep Dive/14. Max marginal relevance example selector.mp417.44MB
- 6. Prompt Templates Deep Dive/15. N-gram overlap example selector.mp430.06MB
- 6. Prompt Templates Deep Dive/16. Semantic similarity example selector.mp413.09MB
- 6. Prompt Templates Deep Dive/17. Partial prompt templates.mp415.73MB
- 6. Prompt Templates Deep Dive/2. Multi-input prompt templates.mp420.38MB
- 6. Prompt Templates Deep Dive/3. Chat prompt template.mp421.57MB
- 6. Prompt Templates Deep Dive/4. Serializing prompts.mp410.55MB
- 6. Prompt Templates Deep Dive/5. Zero-shot prompts.mp421.09MB
- 6. Prompt Templates Deep Dive/6. Custom prompt templates.mp431.22MB
- 6. Prompt Templates Deep Dive/7. Prompt pipelining.mp425.78MB
- 6. Prompt Templates Deep Dive/8. Chat prompt pipelining.mp413.17MB
- 6. Prompt Templates Deep Dive/9. Prompt composition.mp418.03MB
- 7. Prompting Techniques/1. Chain of thought.mp438.91MB
- 7. Prompting Techniques/2. Self-consistency.mp429.64MB
- 7. Prompting Techniques/3. Self-ask.mp450.98MB
- 7. Prompting Techniques/4. ReAct.mp439.69MB
- 7. Prompting Techniques/5. RAG.mp480.69MB
- 7. Prompting Techniques/6. FLARE.mp450.52MB
- 7. Prompting Techniques/7. Plan and execute.mp456.84MB
- 8. Prompt Management a.k.a. PromptOps/1. Prompt management.mp45.62MB
- 8. Prompt Management a.k.a. PromptOps/2. LangSmith.mp43.79MB
- 8. Prompt Management a.k.a. PromptOps/3. LangSmith walkthrough.mp424.45MB
- 8. Prompt Management a.k.a. PromptOps/4. Prompt versioning in LangSmith.mp440.7MB
- 8. Prompt Management a.k.a. PromptOps/5. LangSmith deep dive.mp448.55MB
- 8. Prompt Management a.k.a. PromptOps/6. Managing prompt length for agents.mp433.02MB
- 9. The LLM Landscape/1. Applications of language models.mp410.05MB
- 9. The LLM Landscape/2. The LLM landscape.mp411.86MB
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