I Tried Dify for RAG. The Experience Was Surprisingly Poor.

I recently tested Dify while evaluating RAG chatbot platforms for a topic I’m personally interested in.

I chose Dify because it is frequently recommended as one of the better platforms for building RAG-based AI applications. It is also open source and has attracted significant attention on GitHub, so I went into the experiment with fairly high expectations.

Unfortunately, my experience was almost the opposite.

The first answer was not just inaccurate — it was completely wrong

I created a chatbot in Dify Studio, uploaded some documents, configured the retrieval conditions, and asked it a question.

The answer was terrible.

This wasn't a minor factual error or an imperfect interpretation. The answer was fundamentally wrong.

What made this more concerning was that the question involved a cultural topic. When dealing with cultural, religious, historical, medical, legal, or similarly sensitive subjects, an answer that confidently says something completely contrary to the underlying source material can be much more problematic than an ordinary chatbot mistake.

Initially, I realized that I had forgotten to upload one relevant document.

Fair enough.

Perhaps the model was answering from its own knowledge because the information wasn't available in the RAG system.

So I uploaded the missing document and asked the same question again.

The answer was still wrong.

That was when I started looking deeper.

The workflow debugger looked promising

One thing I liked about Dify was its workflow view.

For each block, you can inspect things such as:

• the input,
• process data,
• and output.

This is exactly the kind of visibility an AI application builder needs. When an agent produces a bad answer, I want to know why.

So I traced the execution block by block.

Unfortunately, this exposed another problem: there was plenty of information, but not necessarily the right information.

For example, the RAG step contained a large "process data" section filled with generic implementation details and values such as latency, prompt bytes, counters, and other internal fields.

Technically, this is data.

But as a developer debugging an AI system, much of it didn't help me answer the questions I actually cared about:

What did the system search for?

Why were these chunks selected?

Which documents did they come from?

How relevant were they?

What information was ultimately passed to the model?

Those are the things that help explain a bad RAG response.

The retrieval itself was poor

When I inspected the RAG output, I could see the retrieved chunks.

And this appeared to be one of the core problems.

The chunks being retrieved were simply not good enough for the question.

I don't know the exact internal retrieval architecture Dify was using in my configuration, but compared with other vector-search and RAG systems I have tested, the retrieval quality in this experiment was noticeably worse.

That matters enormously.

People often evaluate RAG systems by looking at the final LLM answer. But the quality of a RAG application is heavily dependent on what happens before the model generates that answer.

If the retrieval layer gives the model the wrong context, even an excellent LLM is being asked to reason from poor evidence.

But in my test, there was an additional problem.

Even the retrieved context didn't justify the final answer

I examined the chunks that Dify retrieved.

Then I looked at the answer generated by the chatbot.

The final answer didn't even seem to logically follow from the retrieved information.

That is a different class of failure.

Now there are potentially two things to debug:

Retrieval failure

The system retrieves information that isn't sufficiently relevant to the user's question.

Generation/reasoning failure

The model receives some relevant information but still produces an answer that is inconsistent with it.

In my test, I appeared to be dealing with both.

And this is where observability becomes extremely important.

AI debugging needs something better than "Input → Process Data → Output"

The traditional workflow concept of:

Input → Process → Output

is technically correct, but I don't think it is sufficient anymore for debugging agentic and RAG applications.

For an AI tool invocation, I want to see something closer to:

Context What conversation state and information was available when this step ran?

Instructions What exactly was the model or tool asked to do?

Tool input What query or structured parameters were sent to the retriever?

Retrieval Which chunks were selected, from which sources, and with what relevance information?

Model context What information was actually passed to the model after retrieval?

Model output What did the model produce from that information?

That tells me a story.

A large JSON dump does not.

The purpose of observability shouldn't simply be to expose everything happening internally. It should help a developer understand why the system behaved the way it did.

That distinction becomes increasingly important as AI applications become more complex.

More information does not necessarily mean better observability

This was probably my biggest disappointment with the debugging experience.

Dify exposes quite a lot of data.

That sounds good.

But I found myself looking through information that wasn't organized around the decisions I was actually trying to understand.

Good AI observability should progressively answer:

What did the system know?

What did it decide to do?

Why did it do that?

What did the tool return?

What evidence was given to the model?

How did that evidence become this answer?

That is much more useful than exposing internal fields simply because they are available.

This surprised me because Dify is such a prominent project

What makes the experience particularly interesting is Dify's reputation.

It is open source, widely discussed, and has attracted a very large developer community.

That is a major achievement.

And Dify clearly offers a lot beyond simple RAG: workflows, model integrations, tools, agents, knowledge bases, and an application-building environment.

So this isn't a claim that Dify as a project has no value.

It is a much narrower observation:

In the RAG chatbot I built and tested, the actual answer quality and my ability to diagnose incorrect answers were significantly below what I expected from a leading AI application platform in 2026.

That distinction matters.

The real benchmark for RAG isn't how many features the platform has

After this experiment, I think we sometimes evaluate RAG products using the wrong criteria.

We look at:

• how many models they support,
• how many integrations they have,
• whether they have a visual workflow builder,
• whether they support agents,
• whether they are open source,
• how customizable the pipeline is,
• and how sophisticated the UI appears.

Those things are useful.

But ultimately a RAG chatbot has two very basic responsibilities:

Retrieve the right information.

Generate an answer that is faithful to that information.

And when either of those fails:

Make it easy for the developer to understand why.

Everything else is secondary.

RAG still has a long way to go

This experiment also reinforced something I've increasingly noticed while building AI applications.

We talk about agentic AI as though the fundamental problems have already been solved and the remaining work is mostly about connecting tools and designing workflows.

I don't think that's true.

Even something as apparently mature as:

Question → retrieve relevant document chunks → answer from those chunks

can still fail badly.

And when it fails, developers still have to dig through several layers of abstraction to understand what happened.

That tells me there is still a lot of room for improvement — not just in models, but in retrieval systems, orchestration, observability, and developer tooling.

A RAG platform shouldn't merely make it easy to build a chatbot.

It should make it easy to build a chatbot that is reliable.

And equally importantly, when it isn't reliable, it should make the reason immediately visible.

Based on my experience testing Dify, we are not there yet.