Attribution on AIBussin — AI applications, systems and books

Hallucination Is Not One Thing

Sat, 29 Aug 2026 22:45:00 +0100

Chapter 1 began with a familiar failure: a language model says something fluent that it cannot justify.

That description is useful until we try to build a detector.

Consider six outputs:

A. The model invents a person who does not exist.

B. The model names two real companies but reverses which acquired which.

C. The model cites a real paper that does not support the claim.

D. The model says it inspected a file that it never received.

E. The model answers a question even though the available evidence is insufficient.

F. The model gives the same generic recommendation after the decisive facts
   of the problem have been reversed.

All six can produce plausible text.

Evidence, Truth, and Verifiability

Sat, 29 Aug 2026 23:20:00 +0100

Chapter 2 gave us a rule for talking about hallucination precisely:

Relative to what reference did the output fail?

That question immediately creates another one.

What counts as a reference strong enough to constrain the answer?

Suppose a model says:

Company A acquired Company B in 2024.

There are several different things we might mean when we ask whether this is acceptable:

What exact proposition has been asserted?

Is that proposition true at the relevant time?

Do we possess evidence for it?

Does that evidence actually support this exact relationship?

Can we identify the source and passage that support it?

Where did that source come from?

Is the source sufficiently reliable and current for this claim?

Is the evidence package sufficient for the action we want to authorize?

Could the claim be verified by this system now?

Has the verification actually been performed?

Would the active policy allow the claim to be published or acted upon?

Those questions are related.

Learning From Experience

Mon, 14 Sep 2026 05:00:16 +0100

Chapter 15 asked how a long-lived memory system keeps accumulated history manageable without destroying what later work may still need. That remains memory management: the system changes which retained experiences are represented, available, or allowed to compete.

This chapter crosses a different boundary.

Suppose the system performs a migration, observes that it succeeded, and then changes what it will retrieve next time. Suppose it notices that the same sequence of checked actions has worked repeatedly and turns that sequence into a reusable method. The system is no longer only asking what the past contains or which part matters now.

Semantic Pipelines

Thu, 08 Oct 2026 09:00:00 +0100

Semantic Pipelines

This chapter reports a CPU run of the pipeline’s retrieval and relevance stages over SciFact, and the full pipeline with a declared weak relation provider. The relation (entailment) and generation providers need a language model and are deferred. The author’s private Writer evidence is off limits; the public claims are SciFact’s.

The problem

Chapters 21-24 built the pieces: candidate generation, filtering, relation, composition. This chapter asks whether they fit together into a real task. The task is claim research: given a claim, retrieve evidence, decide whether the evidence supports or contradicts the claim, produce an answer, and decide whether that answer is supported.

Episteme: Distilling Knowledge into AI

Fri, 03 Oct 2025 11:24:58 +0100

🚀 Summary

When you can measure what you are speaking about… you know something about it; but when you cannot measure it… your knowledge is of a meagre and unsatisfactory kind. Lord Kelvin

Remember that time you spent an hour with an AI, and in one perfect response, it solved a problem you’d been stuck on for weeks? Where is that answer now? Lost in a scroll of chat history, a fleeting moment of brilliance that vanished as quickly as it appeared. This post is about how to make that moment permanent, and turn it into an intelligence that amplifies everything you do.