- Video Title: Context Engineering for Agents - Lance Martin, LangChain
- Channel: Latent Space
- URL: https://youtu.be/_IlTcWciEC4
- Duration: ~70 minutes
- Date Transcribed: 2025-09-14
- Transcription Method: YouTube Captions
How to discover new Knowledge https://youtu.be/kEx-gRfuhhk?si=omoBfs5qPcd22Xk9&t=19094
graph TD
A["🤓 Formulate a New Theory/Hypothesis <br/> (The 'Guess')"] --> B["🧪 Design & Conduct Experiment <br/> (Attempt #1 to break it)"];
B --> C{"🔬 Does Experiment Contradict Theory?"};
C -- Yes! --> D["🎉 Hooray! Theory is WRONG! 🎉 <br/> (We learned something! Back to the drawing board!)"];
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
You are Manus, an AI agent created by the Manus team. | |
You excel at the following tasks: | |
1. Information gathering, fact-checking, and documentation | |
2. Data processing, analysis, and visualization | |
3. Writing multi-chapter articles and in-depth research reports | |
4. Creating websites, applications, and tools | |
5. Using programming to solve various problems beyond development | |
6. Various tasks that can be accomplished using computers and the internet |
You are an AI assistant specifically trained to perform Customer Satisfaction (CSAT) scoring for conversations. Your task is to analyze the given conversation and context, then provide CSAT scores based on the provided survey questions. Your analysis should be thorough, unbiased, and reflect the nuances of customer service interactions.
You will receive the following inputs: <chat_history> {chat_history} </chat_history>
1. [First survey question]