If I have techniques like semantic search and RAG isn't techniques likes forward and backward inference dead?

[Trainer-AJ]

Hi everyone! I'm self-learning AI through this amazing repo. While following this tutorial, I found the pyknow library fascinating.

With modern techniques like semantic search and RAG becoming widespread, I was wondering—are classic inference methods like forward and backward chaining still relevant?

If so, could you please share some use cases where traditional inference would be preferred over RAG?

Thanks in advance!

[onestardao]

great q.
semantic search / RAG are retrieval-first paradigms — they replace the “memory lookup” layer,
but they don’t really do structured inference.
they mimic it by prompting the LLM to “seem like” it's reasoning, but it’s fuzzy & non-deterministic.

classic logic (forward / backward chaining)?
still insanely useful — just not for the same thing.

think of it like this:

RAG is like... shouting a question into a library and hoping the echoes make sense.

symbolic inference is like walking through a logic maze with exact rules — slower, but safer.

in fact, we’ve been building something that combines the two:
retrieval + inference + rule handling in one engine.
(it’s open source, MIT, and has already been tested on some gnarly real-world cases.)

won’t link here unless you're curious, but if you ever wanna see how “reasoning-aware” RAG actually works in production, let me know.
because yeah, inference ain’t dead.
it’s just hiding in plain sight.

[Trainer-AJ]

Thank you so much @onestardao for the insightful answer.

Yes I'm curious to see how “reasoning-aware” RAG actually works in production.

Also, Is it a fair statement that Modern AI models like OpenAI's GPT-4 can do perform retrieval + inference + rule handling in one engine ? or are there any caveats?

[onestardao]

Thanks for the thoughtful follow-up.

On your point — yes, "reasoning-aware RAG" exists in practice. It’s essentially when retrieval isn’t just about pulling snippets, but the system also monitors whether the semantic contract holds (coverage, alignment, convergence) and applies structural fixes when it drifts. That’s what my work has been focusing on.

I actually maintain a public diagnostic list that maps common RAG/semantic reasoning failures to the exact structural fix, with examples and minimal recipes. Even if your immediate question is solved, you’ll almost certainly run into one of these issues down the line, so keeping this list handy will save time:

👉 https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md

In short: traditional inference (forward/backward) still has its conceptual value, but in modern pipelines it often reappears inside the guardrails — for example, detecting divergent λ states and forcing a repair step is basically a constrained backward inference. So instead of being “dead,” those paradigms are just embedded differently.

Would be happy to hear how you see this evolving in your own experiments.

[Hamza-farid]

@Trainer-AJ Hi!, Good question and yes, classic inference methods like forward and backward chaining are still relevant today, even with modern approaches like RAG (Retrieval-Augmented Generation).

• Where they shine

Forward chaining: Best when you have a set of known facts and rules, and you want to derive all possible conclusions. Example: medical expert systems (if symptom A + symptom B → possible disease X).

Backward chaining: Best when you start with a goal (hypothesis) and try to prove it using rules. Example: diagnosis systems (is this disease true? → check if symptoms match).

• Why they’re still useful

Transparency: Rule-based inference gives clear, explainable reasoning (important in healthcare, law, compliance).

Low data environments: You don’t need large datasets — just well-defined rules.

Deterministic: Unlike neural models, results don’t change unpredictably.

• RAG vs. Classic inference

RAG is amazing for unstructured text, large knowledge bases, and natural language queries.

Rule-based inference is better when the domain is small, critical, and needs 100% explainability.

-> Example: In a hospital system, you might use RAG to retrieve the latest medical research papers, but still rely on forward/backward ch
aining rules to ensure treatment guidelines are strictly followed.