1️⃣ HyDE (Hypothetical Document Embeddings)

🔹 What is HyDE?

HyDE is a retrieval technique where the system first imagines a perfect answer to the query and then uses that imagined answer to retrieve real documents.

Instead of embedding the question, you embed a hypothetical answer.

🔹 Why HyDE Exists

User questions are often:

• Short

• Vague

• Poorly aligned with document language

Documents, however, are:

• Long

• Structured

• Answer-oriented

HyDE bridges this mismatch.

🔹 How HyDE Works (Intuitively)

1. User asks a question

2. LLM generates a hypothetical ideal answer

3. That answer is embedded

4. Vector search is performed using this embedding

5. Real documents matching that “ideal answer” are retrieved

🔹 Real-World Analogy

You don’t search a library using a question like:

“How does kidney failure progress?”

You search using a paragraph describing kidney failure.

HyDE auto-writes that paragraph for you.

🔹 Strengths

• Works extremely well for vague queries

• Improves recall dramatically

• Especially useful in scientific / medical / technical RAG

🔹 Weaknesses

• Depends on LLM quality

• Can introduce bias

• Extra generation cost

🔹 Best Use Case

• Research RAG

• Medical / legal QA

• Exploratory queries

2️⃣ Window Search (Sliding Window Retrieval)

🔹 What is Window Search?

Window search retrieves neighboring chunks around a matched chunk to preserve context.

Instead of retrieving isolated chunks, you retrieve a window of context.

🔹 Why Window Search Exists

Chunking breaks:

• Narrative flow

• Logical continuity

• Cause-effect relationships

Window search fixes this.

🔹 How It Works

1. Retrieve a relevant chunk

2. Also fetch:

   • Previous chunk

   • Next chunk

3. Combine them into one context window

🔹 Intuition

Reading only one paragraph from a book rarely gives full meaning.
Window search gives you the paragraph before and after.

🔹 Strengths

• Preserves context

• Improves answer coherence

• Simple and effective

🔹 Weaknesses

• Adds tokens

• May include irrelevant text

🔹 Best Use Case

• PDFs

• Policies

• Books

• Manuals

3️⃣ Self-Query Retriever

🔹 What is Self-Query Retrieval?

A self-query retriever allows the LLM to:

• Understand the user’s intent

• Extract structured filters

• Generate the retrieval query itself

The LLM becomes the query planner, not just the answer generator.

🔹 Why It Exists

Users mix:

• Natural language

• Implicit constraints

• Metadata requirements

Example:

“Show me beginner-level Python tutorials after 2022”

This includes:

• Topic

• Difficulty

• Time filter

🔹 How It Works

1. LLM parses user query

2. Extracts:

   • Semantic query

   • Metadata filters

3. Executes filtered retrieval

🔹 Intuition

It’s like an intelligent librarian who understands what you really want and applies filters automatically.

🔹 Strengths

• Very powerful for enterprise RAG

• Handles structured + unstructured data

• Reduces irrelevant results

🔹 Weaknesses

• Depends on clean metadata

• Needs schema alignment

🔹 Best Use Case

• Product catalogs

• Course platforms

• Enterprise document search

4️⃣ Contextual Compression Retrieval

(Often confused as “Contractual”; correct term is Contextual)

🔹 What is Contextual Compression?

Contextual compression shrinks retrieved documents to keep only the parts relevant to the query.

Retrieval stays the same — context gets compressed.

🔹 Why It Exists

Even good retrieval often returns:

• Long documents

• Partially relevant sections

LLMs have:

• Token limits

• Cost constraints

🔹 How It Works

1. Retrieve documents

2. Use:

   • LLM

   • Extractor

   • Reranker

3. Remove irrelevant sections

4. Pass only high-signal text to LLM

🔹 Intuition

Instead of handing someone a full book, you highlight only the important sentences.

🔹 Strengths

• Saves tokens

• Reduces hallucination

• Improves precision

🔹 Weaknesses

• Additional compute

• Over-compression risk

🔹 Best Use Case

• Long documents

• Token-sensitive RAG

• High-cost LLMs

5️⃣ RAG Fusion (Multi-Query Fusion)

🔹 What is RAG Fusion?

RAG Fusion improves retrieval by:

• Generating multiple query variants

• Retrieving for each

• Merging and deduplicating results

One question → many perspectives → better recall

🔹 Why RAG Fusion Exists

Single queries suffer from:

• Wording bias

• Vocabulary mismatch

• Narrow perspective

🔹 How It Works

1. LLM rewrites query into multiple forms

2. Each query retrieves documents

3. Results are merged

4. Reranked or deduplicated

🔹 Intuition

You ask:

“How to manage diabetes?”

But also search:

• “Diabetes treatment guidelines”

• “Blood sugar control methods”

• “Lifestyle changes for diabetes”

More doors → more relevant knowledge.

🔹 Strengths

• Massive recall improvement

• Reduces retrieval blind spots

• Excellent for research RAG

🔹 Weaknesses

• Higher cost

• More latency

• Needs reranking

🔹 Best Use Case

• Knowledge-heavy systems

• Scientific QA

• Open-domain RAG

🧠 How These Fit Together (Big Picture)

They are not competitors — they are complementary tools.

🎯 Final Intuition Summary

• HyDE → “Imagine the answer first”

• Window Search → “Read around the paragraph”

• Self-Query → “Let the system understand filters”

• Contextual Compression → “Keep only what matters”

• RAG Fusion → “Search from multiple angles”

🏁 One-Line Takeaway

Advanced RAG retrieval is not about finding more data — it’s about finding the right data, in the right shape, with the right context.