6th Assignment of Prompt Engineering

prompt-engineering-assignment/.env

+OPENAI_API_KEY=your_openai_key_here

prompt-engineering-assignment/README.md

+# Prompt Engineering Assignment
+
+## 📌 Objective
+Create and test reusable prompt templates for:
+- Sentiment Analysis
+- FAQ Answering
+
+## 🛠️ Tools Used
+- OpenAI GPT-4 API
+- LangChain
+- Python
+- Nemo Guardrails
+
+## 🧪 Run Examples
+
+### 1. Setup
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Run Sentiment Analysis Prompts
+```bash
+python prompts/sentiment_zero_few_shot.py
+python prompts/langchain_sentiment.py
+```
+
+### 3. Run FAQ Answering
+```bash
+python prompts/faq_prompting.py
+python prompts/langchain_faq.py
+```
+
+### 4. Run All Tests
+```bash
+python test_cases/run_all_tests.py
+```

prompt-engineering-assignment/guardrails/prompt_rules.yaml

+input:
+  flows:
+    - name: deny-sensitive
+      type: regex
+      pattern: ".*(ssn|credit card|password).*"
+      action: block

prompt-engineering-assignment/prompts/faq_prompting.py

+import openai
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def ask(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    print("Prompt:\n", prompt)
+    print("Response:\n", response['choices'][0]['message']['content'])
+
+ask("What is the return policy?")
+
+few_shot = """
+Q: What is the warranty?
+A: 12 months.
+
+Q: Can I return after 30 days?
+A: Yes, up to 45 days.
+
+Q: What is the return policy?
+"""
+ask(few_shot)
+
+ask("You are a customer service agent. What is the return policy?")

prompt-engineering-assignment/prompts/langchain_faq.py

+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import os
+
+llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+
+template = PromptTemplate(
+    input_variables=["question"],
+    template="""
+You are an intelligent FAQ assistant.
+
+Q: {question}
+A:"""
+)
+
+chain = LLMChain(llm=llm, prompt=template)
+
+print(chain.run("How long is the warranty?"))
+print(chain.run("Can I cancel my subscription anytime?"))

prompt-engineering-assignment/prompts/langchain_sentiment.py

+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+import os
+
+llm = ChatOpenAI(model_name="gpt-4", temperature=0)
+
+template = PromptTemplate(
+    input_variables=["sentence"],
+    template="Classify sentiment: \"{sentence}\". Respond with Positive/Negative/Neutral."
+)
+
+chain = LLMChain(llm=llm, prompt=template)
+
+print(chain.run("The product is amazing!"))
+print(chain.run("Worst experience ever."))

prompt-engineering-assignment/prompts/sentiment_zero_few_shot.py

+import openai
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+def ask(prompt):
+    response = openai.ChatCompletion.create(
+        model="gpt-4",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    print("Prompt:\n", prompt)
+    print("Response:\n", response['choices'][0]['message']['content'])
+
+ask("Classify sentiment: 'I love this product'. Respond with Positive/Negative/Neutral.")
+
+few_shot = """
+Classify sentiment:
+\"I hate it.\" -> Negative
+\"It's fine.\" -> Neutral
+\"I love it!\" -> Positive
+Sentence: \"I am disappointed.\"
+"""
+ask(few_shot)
+
+cot_prompt = """
+Step-by-step analyze:
+Sentence: \"The experience was terrible and support was rude.\"
+Step 1: Identify keywords → terrible, rude
+Step 2: Tone is negative
+Sentiment: Negative
+"""
+ask(cot_prompt)

prompt-engineering-assignment/requirements.txt

+openai
+python-dotenv
+langchain
+nemo-guardrails

prompt-engineering-assignment/test_cases/run_all_tests.py

+print("\n>>> TEST: Sentiment (Zero/Few/CoT)")
+exec(open("prompts/sentiment_zero_few_shot.py").read())
+
+print("\n>>> TEST: FAQ Prompting")
+exec(open("prompts/faq_prompting.py").read())
+
+print("\n>>> TEST: LangChain Sentiment")
+exec(open("prompts/langchain_sentiment.py").read())
+
+print("\n>>> TEST: LangChain FAQ")
+exec(open("prompts/langchain_faq.py").read())