whetstone_RSA/presentations/rsa_tiny_models_talk.md

Tiny Models For Bounded NLP Decisions

whetstone_RSA
Fast-slide draft for a 5-10 minute programmer talk

This version is intentionally structured as many short slides with minimal text. The goal is to support a visual talk with one idea per slide.

Slide 1: Title

Tiny Models For Bounded NLP Decisions

whetstone_RSA

Turning messy user requests into deterministic tool calls

Visual idea: Large title, small diagram of user request -> contract -> tool

Speaker notes: This talk is about a middle layer between regex and LLMs.

Slide 2: A Common Shape

Users type messy things.

Software needs clean actions.

Visual idea: Messy speech bubble on left, clean typed API object on right

Speaker notes: This shows up all over software.

Slide 3: The Gap

We often have:

• fuzzy input
• bounded output
• deterministic execution

Visual idea: Three stacked boxes with arrows

Speaker notes: That middle combination is the important one.

Slide 4: Today’s Typical Options

• regex
• rules
• LLM prompt

Visual idea: Three boxes, with regex looking brittle and LLM looking oversized

Speaker notes: Most teams bounce between brittle and oversized.

Slide 5: Regex Is Cheap

Regex and rules are:

• fast
• local
• inspectable

Visual idea: Green checkmarks next to those three traits

Speaker notes: This is why people keep reaching for rules.

Slide 6: Regex Is Also Annoying

Regex and rules are:

• brittle
• tedious
• bad at paraphrases

Visual idea: Same rule box cracking under many user phrasings

Speaker notes: The moment users say the same thing many ways, rules get ugly.

Slide 7: LLMs Feel Great

LLMs are:

• flexible
• natural-language friendly
• good with paraphrases

Visual idea: Many different speech bubbles converging successfully

Speaker notes: This is why people like them.

Slide 8: LLMs Are Often Overkill

For bounded tasks, LLMs can be:

• expensive
• heavy
• hard to control

Visual idea: Huge engine powering a tiny gear

Speaker notes: A lot of current usage is more model than the task actually needs.

Slide 9: The Missing Middle

There should be a middle layer.

Visual idea: regex -> RSA -> LLM

Speaker notes: That middle layer is what RSA is for.

Slide 10: What RSA Is

RSA is a contract layer for bounded semantic decisions.

Visual idea: Contract sheet icon between user and tool

Speaker notes: Not a chatbot. Not a search engine. A decision contract layer.

Slide 11: What RSA Does

RSA takes:

• messy input
• bounded schema
• optional context

Visual idea: Three inputs feeding one box

Speaker notes: The schema is what keeps it narrow.

Slide 12: What RSA Returns

RSA returns:

• action
• slots
• confidence
• abstain or escalate

Visual idea: Typed JSON-like result

Speaker notes: The output is structured, not freeform prose.

Slide 13: The Core Pattern

Natural language
-> semantic interpretation
-> bounded contract
-> deterministic software

Visual idea: Simple four-step pipeline

Speaker notes: The model interprets. The software executes.

Slide 14: Example Query

show me the failed builds from yesterday

Visual idea: One big centered query bubble

Speaker notes: This is the kind of input users naturally want to type.

Slide 15: Example Contract

Becomes:

• action: search_builds
• status: failed
• date: yesterday
• confidence: 0.93

Visual idea: Search query transformed into structured object

Speaker notes: This is not generation. It is semantic interpretation into a contract.

Slide 16: Example Execution

Then ordinary software:

• runs the query
• renders the results
• logs the action

Visual idea: Database, search, and UI boxes after the contract

Speaker notes: Once the contract exists, it is just normal software again.

Slide 17: Why This Can Stay Tiny

The output is bounded.

Visual idea: Huge input cloud mapped into a small action menu

Speaker notes: The model does not need to generate arbitrary text.

Slide 18: This Is Closer To Classical NLP

• intent classification
• entity extraction
• slot filling
• query interpretation

Visual idea: Old-school NLP pipeline icons

Speaker notes: This is often closer to smart search than to modern chatbot behavior.

Slide 19: Search Bar Example

Make a search/help bar feel intelligent.

Stay local.

Stay bounded.

Visual idea: Application search bar with local chip or small-device icon

Speaker notes: This is one of the clearest examples for programmers.

Slide 20: Search Bar Inputs

Users type:

• where do I change export settings
• find the failed import from last week
• how do I reset my API token

Visual idea: Three stacked queries in a fake app UI

Speaker notes: These are messy, but the outputs are still bounded.

Slide 21: Search Bar Outputs

RSA decides:

• mode
• entity
• filters
• confidence

Visual idea: Four labeled output chips

Speaker notes: It interprets what subsystem should run and how.

Slide 22: Search Bar Execution

Deterministic systems then:

• open settings
• run filtered search
• show help docs

Visual idea: Three deterministic destination panels

Speaker notes: RSA interprets the request and fills the structured call.

Slide 23: Where Else This Fits

• command palettes
• workflow routing
• support triage
• tool selection
• template selection

Visual idea: Grid of small app examples

Speaker notes: Same shape, different domain.

Slide 24: WhetstoneDSL / WHIMP

This fits:

• whetstone_DSL
• WHIMP

Visual idea: Two case-study boxes with arrows into RSA

Speaker notes: Those systems have many bounded decisions that are painful to express as pure rules.

Slide 25: A Lot Of “LLM Workflows” Are Really This

• classification
• routing
• extraction
• slot filling

Visual idea: Big prompt window collapsing into four small tasks

Speaker notes: A lot of LLM spend is really being used as fuzzy routing over deterministic software.

Slide 26: The Enterprise Angle

Many teams use giant prompts and context windows

for tasks that are actually bounded.

Visual idea: Huge prompt box compared to small contract box

Speaker notes: That is the efficiency argument.

Slide 27: The Careful Claim

RSA does not replace all LLM reasoning.

RSA replaces bounded semantic decisions.

Visual idea: Two circles: bounded decisions inside scope, open-ended reasoning outside scope

Speaker notes: Keep the claim narrow and defensible.

Slide 28: Large Context Caveat

RSA is best when the decision can use:

• compact context
• retrieved context
• distilled context

Visual idea: Large document pile reduced to a few selected cards

Speaker notes: For large-context tasks, pair RSA with retrieval or preprocessing.

Slide 29: Another Example Near The Edge

Script generation can sometimes be:

• template selection
• slot filling
• deterministic rendering

Visual idea: Prompt -> template -> filled script

Speaker notes: Some “code generation” is really hidden template filling.

Slide 30: The Main Idea

Use a tiny model only where deterministic software still needs help.

Visual idea: Small model block at the routing boundary only

Speaker notes: That is the whole philosophy.

Slide 31: Closing

whetstone_RSA makes software semantically smarter

without turning everything into a chatbot.

Visual idea: Final pipeline graphic, clean and minimal

Speaker notes: That is the punchline I want people to leave with.