The Future of Knowledge Q&A: Breaking Through the Limitations of Traditional RAG

The traditional RAG (retrieval enhancement generation) technology uses the vector knowledge base, based on the semantic understanding ability of large language model, and filters content from the knowledge base through disassembly of problems and vector search. However, there are obvious bottlenecks in this method:

1.  Recall is incomplete: key information may be missed;

2.  Recall is inaccurate: return a large amount of irrelevant content;

3.  Context exceeds the limit: The recall content exceeds the model processing capacity.

These questions will lead to bias in understanding the large language model and ultimately output inaccurate answers.

More importantly, answers to vertical fields are often hidden deep in the text. We divide the answers into four categories:

1. Surface Q&A: The answers are directly displayed in a single-segment text (such as "the date of release of a certain policy");

2. Summary Q&A: The answers are scattered in multiple texts and need to be summarized (such as "the three major advantages of a certain solution");

3.  In-depth Q&A: The answers need to be reasoned in combination with background knowledge (such as "the root cause of a certain experiment');

4. Relationship Q&A: The answers rely on the linkage chain of entities, attributes, and relationships (such as "the impact of a failure on upstream and downstream systems").

Traditional RAGs have limited effects in vertical fields for two reasons:

1.  General large models lack domain expertise;

2.  Professional answers are often hidden in texts and need to be associated with complex premise knowledge.

The ultimate form of knowledge questions and answers should simulate human thinking mode:

1. Dual-track knowledge storage

1.1. Vector library: store original text (papers, manuals, etc.);

1.2. Knowledge graph: extract the ** entities, attributes, and relationship triples in text through the domain model, store them in the graph database (such as Neo4j), and build a structured knowledge network.

2.  In-depth problem analysis

Disassemble the entities in the problem (such as "device A"), attributes (such as "temperature threshold"), relationships (such as "cause of the cause"), scenes (such as "high temperature environment") and core problems (such as "failure").

3. Multimodal answer generation

Dynamic combination according to the type of problem:

*   Vector search (surface/summary question);

*   Graph query (depth/relational question, such as path inference, attribute filtering);

*   Big model summary and reasoning.

4.  Explanation as the core

Output a complete thinking chain, including:

*   Problem analysis logic;

*   Knowledge retrieval path;

*   Reasoning basis and calculation process.

(Trust comes from transparency: humans need to supervise AI decisions and must understand their working principles)

Here we give a common case of medical diagnostic scenarios:

User question:

"The patient has increased creatinine after taking antihypertensive drug A. He has diabetes and kidney stones. What may be the reason? How to adjust the medication?"

The limitations of traditional RAG:

- Only "side effects instructions for drug A" (missing diabetes association);

- It is impossible to reason from "kidney stones" to "decreased renal metabolic capacity";

- It is difficult to integrate the logical chain of "Medical A + diabetes + kidney stones → renal function risk → elevated creatinine".

The solution process of the ultimate form:

1. Knowledge storage

- Vector library: drug instructions, clinical guidelines, case reports;

- Knowledge graph:

2. Problem analysis

- Entities: `Patient`, `Drug A`, `creatinine`, `Directal stones`;

- Attributes: `Current symptoms = creatinine increase, `Medical history = diabetes + kidney stones`;

- Relationship: `Drug A` may cause `creatinine increase, `Directal/renal stones`, `Decreatinine`, `Directal function`;

- Problem: Attribution analysis + Medication adjustment suggestions.

3. Answer generation

- Graph reasoning:

- Query the side effects of "drug A" → Confirm "creatinine";

- Associate "diabetes" and "kidney stones" → Both point to "decreased renal function";

- Integrated medical rules: **People with decreased renal function are contraindicated to drugs that may damage the kidney**.

- **Vector Supplement**: Searching the literature on "Pharmaceutical A Alternative Solution" and finding that "Pharmaceutical B" is applicable but renal function needs to be evaluated. - Core issues:             - Core issues: The causal relationship between drug A and elevated creatinine + Suggestions for drug adjustment         - Patient risk factors: diabetes (injury glomerulus), renal stones (obstructed urinary tract) → Both lead to weak renal function foundation**         4.2. Knowledge search:           - Map path: Drug A → [side effects] → creatinine elevated ←[risk factors] ← Decreased renal function ←[cause]← Diabetes/renal stones

- Literature supplement: Drug B can be used as a substitute, but it needs to meet eGFR (glomerular filtration rate) >60ml/min

4.3. Reasoning conclusion:

- Increased creatinine is likely due to the additional burden on vulnerable kidneys by drug A;         - Suggested actions:

(1) Discontinue drug A immediately;

(2) Test the current eGFR value;

(3) If eGFR>60, drug B can be replaced (blood pressure needs to be monitored);

(4) If eGFR<60, drug regimen needs to be re-evaluated (such as drug C).

4. Risk warning:

- Ignoring renal function and changing the medicine directly B may cause acute renal injury! Core values ​​of this plan:

The core values ​​of this plan:

- In-depth reasoning: tandem side effects, medical history, physiological mechanisms, rather than mechanical matching keywords;

- Interpretation: Doctors can verify whether the "medicine B recommendation" is based on current renal function data to avoid blindly relying on AI;

- Dynamic decision-making: In combination with test results (eGFR), trigger different drug use branches to achieve personalized plans.