Neuro-Symbolic AI:
A Practitioner's Taxonomy
Escaping the terminology trap and understanding the structural ceiling of LLM-interpreted architectures.
AUTH: COGNISWITCH
The Implementation Trap
The industry made a collective bet: that implementation rigor could overcome architectural limitations. Better prompt engineering. Smarter embeddings. Sophisticated reranking.
The bet didn't pay off.
When your architecture retrieves probabilistically and synthesizes through an LLM, no amount of tuning makes the output deterministic. You can push consistency from 70% to 85%. You cannot push it to 100%. The ceiling is structural.
Critical Insight
"When a regulator audits your decisioning, 'the LLM interpreted the policy this way' is not a defensible position."
The Terminology Trap
The vocabulary is broken. Terms have been stretched until they communicate nothing.
Impact Analysis
"Valid approaches die in procurement because the terminology carries baggage. The cost isn't failed projects. It's misallocated projects."
SEVERITY: CRITICAL
Who Decides What Is True?
LLM
Synthesizes Response
Resolves Ambiguity
"The ceiling is probabilistic. You can improve from 70% to 85%. You cannot reach 100%."
Ontology
Governs Retrieval
Enforces Logic
The Six Dimensions
Not a spectrum. Not "hybrid" in the mushy middle. Different architectures optimize for different things.
LLM-Interpreted
Optimized for ambiguity, speed, and setup. The LLM decides what is true.
Ontology-Governed
Optimized for consistency, traceability, and compliance. The Ontology decides what is true.
The Tradeoff Reality
You don't need to max all six dimensions. You probably shouldn't try. Claiming you need maximum reliability, maximum flexibility, and minimum investment is a sign you haven't defined the problem.
The Diagnostic Question
"If two pieces of retrieved information conflict, how does the system decide which is correct?"
The right architecture isn't just about your problem.
It's a combination of your problem, your implementation process, the autonomy you expect to give AI, the scale at which your business currently operates—and the scale you envision eventually.
This is a long-term view. But that's what it takes to actually get AI into production. Not a weekend hackathon. Not a proof-of-concept that never graduates. A system that runs, scales, and stays accountable.
The full essay maps that landscape—helping you understand the choices you've made, the tradeoffs you've accepted, and the paths still open to you.