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.
Chatbot demos hallucinate on real questions
No way to tell whether answers are getting better or worse
02 / What I build
Capabilitiesin this service.
LLM 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.
Tool 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.
Memory & Context Structuring
Create prompting strategies for single-turn and multi-turn interactions, including context retention, summarization, memory injection, and continuity across workflows.
Safety, Guardrails & Filtering
Implement prompt-level controls that reduce hallucinations, off-topic outputs, unsafe responses, and inconsistent behavior through better instructions and fallback design.
Domain-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
More in GenAI / RAG
06 / Related work
Proof,not promises.
Medical RAG ChatbotLangChain + Pinecone + Flask
AI-Powered Medical Knowledge Assistant Retrieval-Augmented Generation (RAG) Chatbot
- Python
- LangChain
- Pinecone
- OpenAI
- +2
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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.
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.
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.
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.