AI agent is amazing, promising, and useful in many business scenaria. With the increasing capability of large language model such as ChatGPT-4 and opensource models such as Llama3, it beccomes easy to develop LLM based agent to help the human to do tedious work. For example, it can replace coacher with AI agent to train the junior salesman, while the latter can play with the AI agent to improve their pitch skill. What you do is to select suitable opensource tools and models and to write promts, i.e. prompt engineering, for each application scenario. The following image is the screenshot of the role-agent demo what I build.

AI agent is salesman
Human user is a buyer

Ai agent is buyer
Human user is salesman
What you need to build the demo:
- Streamlit: UI design
- Ollama: manage open-source LLM.
- LangChain: build LLM based chat pipeline
- Prompt engineering: according to the task and role, write prompt.
-
Previous
Collection of prompt engineering -
Next
Competitive intelligence in e-commerce: all about product pricing and assortment
FEATURED TAGS
computer program
javascript
nvm
node.js
Pipenv
Python
美食
AI
artifical intelligence
Machine learning
data science
digital optimiser
user profile
Cooking
cycling
green railway
feature spot
景点
work
technology
F1
中秋节
dog
setting sun
sql
photograph
Alexandra canal
flowers
bee
greenway corridors
programming
C++
passion fruit
sentosa
Marina bay sands
pigeon
squirrel
Pandan reservoir
rain
otter
Christmas
orchard road
PostgreSQL
fintech
sunset
thean hou temple in sungai lembing
海上日出
SQL optimization
pieces of memory
回忆
garden festival
ta-lib
backtrader
chatGPT
stable diffusion webui
draw.io
streamlit
LLM
prompt engineering
fastapi
stock trading
artificial-intelligence