Add RSA extraction bootstrap and working notes

presentations/rsa_tiny_models_talk.md

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+# 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.