AI can perform miracles but only if you know how to ask. I’m Subodh, a Prompt Engineer at Nirvana Lab. I’ve wrestled with the fine art of crafting the perfect AI instruction. Sometimes, a single word changes everything other times, even the most polished prompts fail spectacularly. 

So, what really works in real-world AI applications? How do you turn vague ideas into precise, actionable outputs? And what surprising lessons have I learned along the way? Join me as I break down the challenges, breakthroughs, and game-changing use cases that define prompt engineering today. 

What is Prompt Engineering?  

Prompt engineering is the practice of crafting and refining input text (prompts) to guide the output of LLMs effectively. A good prompt can determine the accuracy, reliability, and efficiency of the model’s response. In practice, it involves:  

• Selecting the right format (instructions, examples, questions)  

• Defining task boundaries clearly

• Testing across LLM versions  

• Iteratively improving based on results  

LLMs are nondeterministic and context-sensitive, so prompting isn’t a fire-and-forget task. It’s more like tuning a musical instrument you have to experiment.  

Key Challenges in Prompt Engineering 

While prompt engineering unlocks AI’s potential, businesses face hurdles like ambiguity, bias, scalability, and dynamic adaptability, requiring strategic solutions. 

1. Iterative Testing is Non-Negotiable 

Prompt engineering is an experimental process. What works in a demo may fail in production. Companies like Google DeepMind and OpenAI emphasize A/B testing, user feedback loops, and continuous refinement to optimize prompts. 

2. Domain-Specific Fine-Tuning Yields Better Results 

Generic prompts underperform in specialized fields like legal tech, healthcare, or financial services. Incorporating domain-specific knowledge and terminology significantly improves accuracy. 

Example: 

• Generic Prompt: “Explain blockchain.” 

• Domain-Optimized Prompt: “Explain blockchain in the context of decentralized finance (DeFi), focusing on smart contracts and security implications.” 

3. Hybrid Approaches Outperform Pure AI Reliance 

While AI can automate many tasks, human-AI collaboration often delivers superior results. For instance, content moderation systems use AI for initial filtering but rely on humans for nuanced decisions. 

4. Explainability Enhances Trust 

Businesses need AI systems that provide transparent reasoning, especially in high-stakes industries. Techniques like chain-of-thought prompting (asking AI to explain its reasoning step-by-step) improve interoperability. 

Real-World Use Cases of Prompt Engineering 

Frame-skipping emotion analyzers, multi-stage document parsing, and embedding-based recommendations, how the right prompts make AI work in production. 

Use Case 1: Face Emotion Analyzer for Autistic Children 

The Vision 

One of the most rewarding projects I worked on involved creating a face emotion analyzer using open-source Python libraries like OpenCV, DeepFace, and TensorFlow. This tool was designed to help teachers track the emotional states of autistic children during classes and generate performance reports. 

How LLMs Were Used 

While the computer vision pipeline was primarily based on deep learning models, I used LLMs extensively for: 

• Writing glue code between different libraries 

• Creating logic to convert frame-wise emotions into time-series summaries 

• Designing a user-friendly report format

• Automating file handling and storage logic 

Prompt Example 

You are a Python expert. Write a function that takes a video file path and uses DeepFace to detect and log emotions frame by frame. It should skip frames for performance, handle exceptions, and return the most frequent emotion per minute. 

Challenges 

• Model hallucinations: LLMs would sometimes suggest APIs that didn’t exist in the library versions.

• Library conflicts: Prompted code would install incompatible versions of TensorFlow and DeepFace.

• Performance tuning: LLMs lacked awareness of real-time processing constraints. I had to guide the model to use threading, buffering, or frame skipping.

After several iterations, I found that prompting GPT-4 with extremely specific instructions including expected version numbers and CPU/GPU hints gave the most reliable results. 

Use Case 2: Document Parsing with LLMs 

Scenario 

Document parsing is one of the highest-ROI use cases of generative AI in business. I built a document parser that extracts structured data from unstructured PDFs such as contracts. The LLM served as the reasoning engine to parse and classify content once OCR was completed. 

Prompt Engineering in Action 

For high-quality LLMs (e.g., GPT-4-turbo), one-shot prompts with labeled examples worked well: 

Prompt: 
Extract fields: Name, Date, RFQ Number, Total Award Amount. Format the response in JSON.

Sample document content: 

For smaller LLMs (e.g., GPT-3.5 or open-source models), multiple rounds were needed: 

1. First prompt to extract text blocks 

2. Second prompt to filter by keywords 

3. Third prompt to format into JSON

Problem 

The same prompt gave clean results on GPT-4 but inconsistent formatting and missed fields on other models. I ended up designing multi-stage pipelines with fallback prompts to ensure resilience. 

Use Case 3: Product Recommendation Engine 

Overview 

This was a more traditional business application. Using product descriptions from a retail website, I built a basic recommendation engine using: 

• Embeddings generated by OpenAI’s text-embedding-ada-002 

• Vector similarity search using FAISS 

• Prompt-based profile generation for user personas 

Role of Prompt Engineering

I used LLMs to: 

• Generate synthetic metadata (e.g., category, age group, gender fit) 

• Convert customer feedback into embedding space for personalization

• Rewrite descriptions to suit different marketing campaigns

Prompt Example 

Generate product tags like [Color; Use Case; Age Group; Style] from this description: “Red polka dot umbrella with ergonomic handle.” 

The Challenge of Library Incompatibility 

A recurring issue with LLM-generated code is dependency mismatches. Prompts often yield code snippets requiring conflicting versions of key libraries. For example: 

• TensorFlow 2.x vs. 1.x APIs 

• Incompatibility between DeepFace and opencv-python-headless

• Different output formats for HuggingFace vs. OpenCV face detectors 

Solution: Include the following in the prompt to ensure compatibility: 

Use TensorFlow 2.9+, OpenCV 4.5+, and DeepFace >= 0.0.75. Ensure compatibility. Return pip install commands. 

The Problem with LLM Variance 

LLMs vary significantly in performance: 

• GPT-4: 90% consistent output 

• GPT-3.5: 60% accuracy, often requiring follow-up prompts 

• Open-source LLMs: Frequently lack context or produce hallucinated outputs 

Approach: Implement a version-check pipeline and use retry logic with varied prompts. Write fallback logic using traditional regex if necessary. 

Lessons Learned on Prompt Crafting 

Best practices for prompt crafting: 

Why Prompt Engineering is Not “One Prompt Fits All” 

Myth: One prompt should work across all models. 

• Lower-tier LLMs lack reasoning context. 

• Open-source models often overfit to keyword prompts. 

• GPT-4 may fail without specific expectations. 

Approach: Treat LLMs like external APIs by defining clear specs, using retries, and gracefully degrading. 

Future Directions and Best Practices 

Ideas to Explore: 

• Prompt token budget optimization for latency-sensitive use cases 

• Use of synthetic data + LLM prompts for test automation 

• LLM-powered inline doc generation for rapid prototyping 

• Prompt versioning and evaluation pipelines 

Recommended Tools: 

• Cursor for LLM-integrated coding 

• LangChain for chaining prompt stages 

• Pydantic for validation of LLM-generated data 

• OpenAI functions & tools API for structured responses 

Conclusion 

Hope you enjoyed this deep dive into the world of prompt engineering! Crafting the perfect AI instruction is a blend of experimentation, domain expertise, and structured refinement. From emotion recognition to document parsing, we’ve seen how precise prompts drive real-world impact yet challenges like model inconsistencies and library conflicts keep us on our toes. The future? Smarter pipelines, robust validation, and seamless human-AI collaboration. Keep iterating, and happy prompting!