I’ve been experimenting with structured context injection in conversational LLM systems lately, what some products call “lorebooks,” and I’m starting to think this pattern is more useful than it gets credit for.
Instead of relying on the model to maintain everything through raw conversation history, I set up:
- explicit world rules
- entity relationships
- keyword-triggered context entries
The result was better consistency in:
- long-form interactions
- multi-entity tracking
- narrative coherence over time
What I find interesting is that the improvement seems less tied to any specific model and more tied to how context is retrieved and injected at the right moment.
In practice, this feels a bit like a lightweight conversational RAG pattern, except optimized for continuity and behavior shaping rather than factual lookup.
Does that framing make sense, or is there a better way to categorize this kind of system?
submitted by /u/SolaraGrovehart
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