The Tech Behind The Talk: How Our Chatbot Understands You
                     The Tech BehindThe Talk: How 
Our Chatbot Understands You 
When we started building EnGenie, weweren’t just aiming for a chatbot 
that could talk—we wanted one that could understand.That meant going 
beyond surface-level NLP and diving deep into the mechanics of how 
language models, structured data, and smart routing come together to 
create a truly intelligent assistant. 
Here’s a peek under the hood. 
Natural Language Processing (NLP): The Foundation 
At the heart of EnGenie is a Large Language Model (LLM)—a powerful 
engine trained on vast amounts of text to understand and generate 
human-like language. But we quickly realized that a plain LLM wasn’t 
enough. It could talk, but it didn’t know anything about our company. 
So we added structure. 
Training Data: From Generic To Domain-Specific 
Instead of fine-tuning the LLM (which is expensive and hard to maintain), 
we used Retrieval-Augmented Generation (RAG). This allowed us to: 
1. Chunk our internal documents (like HR policies) into 
manageable pieces. 
2. Embed those chunks into vector representations. 
This way, the chatbot could “look up” relevant information before 
3. Store them in a FAISS vector database for fast retrieval. 
answering—just like a 
human would. 
Smart Query Routing: One Size Doesn’t Fit All 
Not every question needs the same treatment. So we built a routing 
layer powered by the LLM itself. It decides: 
● Policy Questions → Go to the RAG pipeline 
● Holiday Queries → Generate SQL and fetch from 
a database 
● Greetings → Simple LLM response 
● Follow-Ups → Retrieve past conversation context 
This made EnGenie feel smarter and more human—because it responded 
ibnatseendt,  onno t just keywords. 
Decision Trees? More Like Decision Models 
While traditional chatbots use decision trees, we used LLM-based 
decision-making. The model evaluates the query and routes it 
accordingly. This dynamic approach is more flexible and scalable than 
hardcoded trees. 
Speaking Like A Human (Not A Robot) 
Even with the right answers, tone matters. We didn’t want EnGenie to 
sound like a legal document. So instead of fine-tuning, we used prompt 
engineering to shape its personality: 
“You are an AI assistant trained to answer HR questions in a friendly, 
personalized tone…” 
This simple system prompt ensured responses were: 
● Grounded in 
context 
● Conversational 
Ob●s eTrruvstawboirltihtyy : Making The 
Invisible Visible 
We integrated Langsmith to 
tra●ck :Q uery flows (RAG, SQL, 
etc.) ● Token usage and 
cost ● Latency and 
performance ● Prompt 
Thiesx hpeelrpimede unsts d ebug faster, optimize smarter, and stay in control of 
costs. 
Evaluation: Measuring What Matters 
To assess quality, we used RAGAS (Retrieval-Augmented Generation 
Assessment Suite), which scores answers on: 
● Faithfulness 
● Answer 
Relevance 
● Context 
ThiRse gleavaen ucse  a clear picture of how well EnGenie was performing—and 
● Context Recall 
wimhperroev teo.  
Final Thoughts 
Building a chatbot that truly understands you isn’t just about NLP. It’s 
about engineering intelligence—from smart routing and retrieval to tone 
control and observability. 
With EnGenie, we’ve built more than a chatbot. We’ve built a trusted 
assistant that listens, understands, and responds like a real teammate. 
Partner With EnFuse Solutions 
At EnFuse Solutions, we specialize in building intelligent, scalable, and 
human-centric AI systems that enhance business efficiency and user 
experience. With AI-powered automation, our experts help organizations 
leverage technology that truly understands and delivers. 
Ready To Build Your Own Intelligent Assistant? 
Contact EnFuse Solutions today to transform conversations into 
meaningful digital experiences. 
Read more: 
Building Our Own Chatbot: Why We Decided To Go In-House