Beyond Context Windows: Building an LLM with Injectable Layers
Most large language models today — GPT, Claude, Gemini, whatever — are brilliant, but fundamentally stateless. Every time we talk to them, we shovel in context: business rules, data schemas, Markdown files, embeddings, etc. The LLM doesn’t “remember” anything unless we feed it again. That’s expensive, slow, and cognitively inefficient.
What if we could architect something different?
The Idea: Injectable Layers on Top of a Base LLM
Imagine a base model like GPT-5 or Claude 3, but with a modular, cloud-based layer system sitting on top. Each “layer” injects persistent context into the model — not through prompts, but through direct, addressable access at inference time.
Think of it like building an operating system for intelligence.
- The base LLM is the kernel.
- Businesses (or users) can mount “drives” of context — authentication, business rules, live databases, knowledge bases, Markdown skill files, etc.
- The LLM can reference those drives dynamically, without having to re-ingest their contents every time.
This architecture breaks the dependence on token limits. Instead of packing context windows with giant prompts, the model queries context objects, much like an application hitting APIs or memory.
The Conceptual Architecture
Cloud Layer
Business Rules
Authentication / ACL
Data Connectors
Markdown Skill Files
Dynamic
Knowledge Maps
Context Gateway
Handles Model Queries
Routes to Right LLM
Resolves External Data
Injects Metadata
Hooks
Base LLM(s)
GPT / Claude / Mistral
Task-Routed Dynamically
Stateless Core Models
Cloud Layer → Context Gateway → Base LLM(s)
1. Context as Mounted Memory
Instead of stuffing all your context into each prompt, the model can mount context sources like drives. They
live externally but are addressable by a persistent symbolic link (like /biz/rules/hr or
/db/reports/monthly).
2. Separation of Intelligence and Knowledge
The LLM handles reasoning, structure, and linguistic intelligence. The context layers hold truth — data, policy, documentation. The LLM becomes the “brain,” the layers become its “memory cortex.”
3. Skill Injection via Markdown
Instead of retraining models, businesses can author Markdown skill files:
# Skill: Generate Monthly Report
## Description
Produces a financial summary for the given date range.
## Steps
1. Query `/db/finance/reports?period={month}`
2. Format as a markdown table
3. Annotate insights
The model parses these dynamically. Skills can be versioned, swapped, or revoked instantly — like micro-plugins.
4. Live Data Streams
Rather than embedding snapshots of data, this system could use “live connectors” — lightweight adapters that pull from databases or APIs in real time. The model never owns the data; it just reads from it securely.
5. LLM Routing Layer
Why stop at one model? A routing layer can benchmark task types and select the right engine:
- GPT for creativity
- Claude for analysis
- Mistral for coding
- Llama for local offline tasks
You can even hybridize — GPT plans the query, Claude interprets results, Mistral executes structured code.
How It Would Feel to Use
Imagine asking:
“Generate a weekly team summary using yesterday’s sprint board and highlight blockers.”
You don’t send the sprint board data — the LLM already has access via /jira/boards/sprint-42. It
queries, summarizes, formats, and outputs your report instantly.
No long prompt. No lost context. No data leakage. Just reasoning.
Why This Matters
This kind of LLM system would be:
- Composable: Plug in or detach capabilities like Lego bricks.
- Persistent: Context lives with the LLM layer, not inside prompts.
- Secure: Data stays behind gateways with policy-based access.
- Efficient: No re-tokenizing gigabytes of context on every run.
It’s the natural next step toward real artificial reasoning systems — ones that operate with persistent, structured, and dynamic memory.
Closing Thought
LLMs today are like geniuses with amnesia. They can reason incredibly well but forget everything after each conversation.
An injectable-layer architecture would give them long-term structure — a way to know where to find truth, not just how to talk about it.
That’s how we move from prompt engineering to knowledge orchestration.
