🎓 Bonus Exercise – Advanced Techniques for Smarter RAGs

🪄 This is a free-form, bonus exercise. There's no "correct" outcome — only deeper exploration.

You've already built a powerful testing assistant. Now it's time to experiment with advanced Retrieval-Augmented Generation (RAG) strategies and Dify-specific features that make your assistant more intelligent, focused, and useful for testers.

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🧠 Why Go Beyond the Basics?

While standard RAG systems retrieve documents and pass them to a language model, real-world software testing questions require:

• Better focus on relevant information
• More structured reasoning for complex queries
• Dynamic adaptation to different types of questions

This is where combining advanced RAG strategies with Dify blocks unlocks the next level.

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🧪 Part 1 – Advanced RAG Techniques

These are conceptual strategies used in many RAG systems that help with retrieval quality, context relevance, and multi-turn generation.

🪜 1. Parent–Child Retrieval

Why it matters: You often need related items together: a parent epic with child issues, or a test suite with all cases. This improves reasoning and coverage.

How to use it:

• Structure your documents with parent-child metadata (e.g., epic_id, test_suite)
• Filter in Dify's knowledge retriever by metadata field to retrieve children or a whole group

🧹 2. Query Rewriting

Why it matters: Many queries are vague or poorly phrased. Rewriting improves retrieval and LLM generation.

How to use it:

• Use a classifier or prompt node to detect vague queries
• Add system prompts that reformulate the query with more context before retrieval

🔁 3. Iterative or Multi-Query Retrieval

Why it matters: If a query references multiple issues or modules, each subquery deserves its own chunk of context.

How to use it:

• Split queries using a Parameter Extractor
• Loop over values with the Iteration node
• Combine or format answers at the end

🎯 4. Retrieval Filtering and Chunk Design

Why it matters: Filtering based on metadata avoids off-topic results. Chunking well ensures completeness without verbosity.

How to use it:

• During ingestion, assign metadata like component, priority, or type
• Use metadata filters in the Retrieval node
• Experiment with different chunk sizes: smaller for bugs, bigger for concepts

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⚙️ Part 2 – Powerful Dify Blocks to Boost Your Chatbot

These are not RAG strategies per se, but Dify capabilities that improve your assistant's intelligence and control.

📚 1. Parameter Extractor

Extracts structured data (like issue keys, module names, or dates) from user queries. Ideal for filtering or branching logic.

🧠 2. Question Classifier

Classifies user intent (e.g., test generation vs. documentation lookup). Each category can follow a different chatflow.

Try categories like:

• specific_issue
• list_issues
• general_project
• test_related
• test_generation
• unrelated

🔁 3. Iteration

Use it to generate a loop over multiple issues. Great for:

• "Generate test cases for REST-201, REST-202, REST-203"
• "Summarize these five bugs"

⚖️ 4. If/Else Routing

Adds logic to your flow:

• If issue_key exists → go to filtered retrieval
• If query is unrelated → reply with a fallback message

🧰 5. Prompt Templates

Use specialized prompts depending on user intent. For example:

• System prompt for test generation (You are a Gherkin generator...)
• System prompt for architecture questions (You are a system design assistant...)

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🧪 Bonus Challenges

Let's revisit our question classifier. It identifies 6 types of queries. Try customizing each path using the above techniques:

Category	Try This...
specific_issue	Parameter extractor → filtered retriever by issue_key
list_issues	Parameter extractor → iteration over each → summarize or generate
general_project	Dedicated retriever + LLM focused on project scope
test_related	Retrieval filtering by type = test_strategy or tuned prompt
test_generation	Custom prompt + single or iterative test generation
unrelated	Route to fallback LLM with polite redirect message

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🗂️ Download: Preconfigured Bonus Chatflow

You can download a ready-made version of this bonus exercise, including a fully configured Dify chatflow with:

• Question classifier
• Iteration for multi-issue generation
• Metadata-based filters
• Specialized prompts per query type

⬇️ Download Exercise 4 Sample

💡 Import this YAML file into Dify and start tweaking!

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🌟 Wrap-up

If you've made it here, congratulations — you're ready to take Testus Patronus to the next level! Explore, experiment, and summon the full potential of AI-powered software testing.