You will get Automated FAQ AI Agent using LLM & Prompt Engineering
Project details
Most basic FAQ bots just dump user queries directly into an LLM with a single generic prompt, leading to hallucinations, inconsistent tone, and irrelevant answers for out-of-scope questions. Your project stands out because you built a structured, multi-stage pipeline instead of a single prompt-response system.
Key differentiators:
Intent classification before LLM inference — most beginner projects skip this. By classifying intent first, you reduce wasted API calls and prevent the LLM from trying to "answer" things it shouldn't.
Dynamic knowledge base integration — instead of hardcoding answers, your system retrieves relevant context dynamically, making it scalable and easy to update without retraining or rewriting prompts.
Advanced prompt engineering (role-setting + few-shot examples + output formatting) — this shows you understand how to control LLM behavior precisely, not just "ask a question and hope for a good answer."
Conditional routing logic — this is the most technical differentiator. It demonstrates system design thinking (decision logic + AI), not just API usage.
Key differentiators:
Intent classification before LLM inference — most beginner projects skip this. By classifying intent first, you reduce wasted API calls and prevent the LLM from trying to "answer" things it shouldn't.
Dynamic knowledge base integration — instead of hardcoding answers, your system retrieves relevant context dynamically, making it scalable and easy to update without retraining or rewriting prompts.
Advanced prompt engineering (role-setting + few-shot examples + output formatting) — this shows you understand how to control LLM behavior precisely, not just "ask a question and hope for a good answer."
Conditional routing logic — this is the most technical differentiator. It demonstrates system design thinking (decision logic + AI), not just API usage.
Machine Learning Tools
PythonWhat's included
| Service Tiers |
Starter
$35
|
Standard
$75
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 10 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 1 | 2 |
Number of Scenarios | 3 | 5 | 10 |
Number of Graphs/Charts | 0 | 0 | 0 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
About Tabish
AI Engineer
Faisalabad, Pakistan - 7:57 am local time
"Hi, I'm Tabish , a data scientist passionate about uncovering insights and driving decisions with data. I specialize in machine learning, data visualization, and statistical analysis to help organizations make data-driven decisions. Let's connect and explore how data can drive your business
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After purchasing the project, send requirements so Tabish can start the project.
Delivery time starts when Tabish receives requirements from you.
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