Knowledge is spread across PDFs, wikis and inboxes
03 / Services / GenAI / RAG
Prompt EngineeringTurn knowledge into answers.
Prompt engineering for LLMs, chatbots, RAG systems, agents and automation workflows that improves accuracy, consistency, safety and real-world usefulness.
01 / The problem
What usuallygets in the way.
Build AI applications grounded in your documents, with retrieval you can inspect, citations people can check, and evaluation built in.
Chatbot demos hallucinate on real questions
No way to tell whether answers are getting better or worse
02 / What I build
Capabilitiesin this service.
01LLM Behavior & Response Control
Guide models such as GPT, Claude, Gemini, and open-source LLMs using system instructions, few-shot examples, prompt chaining, and output formatting strategies.
02Tool Use & Function Calling
Design prompts that enable AI systems to call APIs, trigger tools, schedule actions, retrieve documents, update CRMs, and interact with external systems.
03Memory & Context Structuring
Create prompting strategies for single-turn and multi-turn interactions, including context retention, summarization, memory injection, and continuity across workflows.
04Safety, Guardrails & Filtering
Implement prompt-level controls that reduce hallucinations, off-topic outputs, unsafe responses, and inconsistent behavior through better instructions and fallback design.
05Domain-Specific Prompt Systems
Tailor prompts for industries such as legal, healthcare, education, HR, e-commerce, and customer support with the right tone, logic, and vocabulary for each use case.
- Ingestion, chunking and retrieval tuned on your content
- Answers with citations and clear fallbacks
- An evaluation set and quality dashboard
03 / How it works
From documents to cited answers
A typical architecture for this kind of work. Scroll to follow the flow, or hover a stage for detail.
Documents
PDFs, pages and tickets, with access rules preserved.
Parsing
Text, tables and structure extracted cleanly.
Chunking
Sections sized for meaning, not a fixed character count.
Embeddings
Vectors that capture what each chunk is about.
Vector database
Fast similarity search with metadata filters.
Retrieval
Hybrid search and re-ranking pick the best context.
LLM
A prompt that answers only from retrieved context.
Answer + citations
Sources shown, so people can verify.
04 / In depth
The details.
Prompt Engineering Services
Design high-performing AI interactions with expert prompt engineering for LLMs, chatbots, RAG systems, agents, and automation workflows. I help businesses create prompts that improve accuracy, consistency, safety, and real-world usefulness.
My Expertise in Prompt Engineering
I specialize in building custom prompt engineering solutions for businesses across legal tech, healthcare, e-commerce, SaaS, and other knowledge-driven industries. My work combines LLM behavior control, structured prompting, tool use, memory design, guardrails, and domain-specific instruction design to create AI systems that are more reliable, context-aware, and aligned with business goals.
- ✍️ Prompt Design & Optimization
- 🧠 LLM Behavior & Response Control
- 🔗 Tool Use & Function Calling
- 🗂 Memory & Context Structuring
- 🛡️ Safety, Guardrails & Filtering
- 🏷️ Domain-Specific Prompt Systems
Prompt Design & Optimization
Craft structured prompts that improve accuracy, tone, clarity, consistency, and task performance across chatbots, assistants, summarizers, and AI workflows.
My Expertise in Data & AI Industry
From raw data to AI-powered decisions - I help businesses implement end-to-end machine learning workflows, analytics dashboards, and automation pipelines.
Hire MeRemote Data & AI Solutions
I’m a freelance Data Science and ML Engineer with 7+ years of experience transforming data into actionable insights. I specialize in building intelligent solutions, custom ML models, and scalable pipelines for startups and SMEs worldwide.
- United States (Remote Projects)
- Germany (AI Consultancy)
- EUROPE (Remote Projects)
- Remote - Available Worldwide
You Can find me here
As an independent Data & AI Engineer, I help companies unlock the full potential of their data. From predictive analytics to workflow automation, I offer strategic and technical support that drives real impact.
Book Free Consultation View Portfolio05 / Tech stack
Tools I usefor this work.
- LangChain
- LlamaIndex
- OpenAI
- Claude
- Pinecone
- FAISS
- FastAPI
06 / Related work
Proof,not promises.
Project screenshotApplication interface
Medical RAG ChatbotLangChain + Pinecone + Flask
AI-Powered Medical Knowledge Assistant Retrieval-Augmented Generation (RAG) Chatbot
- Python
- LangChain
- Pinecone
- OpenAI
- +2
Project screenshotApplication interface
Bird Disease Classification (MLOps)
Built an end-to-end deep learning MLOps pipeline for bird disease classification using TensorFlow/Keras, PyTorch, OpenCV for image processing, FastAPI for model serving, and Docker for containerization. Implemented automated training pipelines, model versioning, and deployment workflows.
Project screenshotApplication interface
U.S. Visa Approval Prediction (MLOps)
Developed a complete MLOps pipeline for visa approval prediction using Python, FastAPI, Docker, AWS (EC2, ECR, S3), XGBoost, and CatBoost. Implemented automated model training, versioning, deployment, and monitoring with CI/CD integration for production-grade machine learning.
Uber Data Engineering ETL Project
- Python
- GCP
- BigQuery
- Mage.ai
Project overview
Uber Data Analytics Pipeline
Built a robust time-series forecasting solution with over 90% accuracy using Python and statistical modeling.It enabled a growing online retailer to better manage inventory, reduce overstock, and forecast seasonal demand shifts.
Project screenshotApplication interface
CareSage - RAG Medical Chatbot
Developed an intelligent medical chatbot using Retrieval-Augmented Generation (RAG) with Flask, LangChain, Pinecone vector database, OpenAI embeddings, and sentence-transformers. The system processes medical PDFs using PyPDF to provide accurate, context-aware responses for healthcare queries with citation support.
Project screenshotApplication interface
n8n Workflow Automation
Built 8 comprehensive automation workflows using n8n platform integrated with Retell AI, GoHighLevel, Twilio, OpenAI GPT-4, and PostgreSQL. Projects included AI Voice Agent for customer service, Multi-Channel Communication Hub, Lead Qualification System, CRM Sync Automation, Content Generation Pipeline, Customer Onboarding Workflow, Voice-Based Appointment Scheduler, and BI Reporting Dashboard.
07 / FAQ
Common questions.
What is a RAG application?
A RAG (retrieval-augmented generation) application answers questions from your own documents. It searches a vector index for the relevant passages and gives them to a language model, so answers are grounded in your content and can cite their sources.
Can RAG answer questions from medical or legal documents?
Yes, with citations so every answer can be checked. I have built a medical document assistant that answers healthcare questions from PDFs with source references. For legal and medical content, I add evaluation sets and refusal rules for questions the documents do not cover.
How do you reduce hallucinations in LLM applications?
Hallucinations drop when answers must come from retrieved sources, cite them, and decline when nothing relevant is found. I measure this with an evaluation set of real questions and run it on every change to prompts, models or data.
GenAI / RAG
Need Prompt Engineering?Let’s scope it together.
Tell me about the goal, the data and the constraints. I’ll reply with questions and a practical first step.