Large Language Models: Field Manual for Epistemic Self-Defense

Large Language Models: Field Manual for Epistemic Self-Defense

Doctrine, Procedures, Constraints


0. Purpose

This document defines the primary strategic use of locally operated large language models.

Not content generation. Not companionship. Not automation of thought.

Primary function: reduce the cost of verifying claims.

Outcome: epistemic self-defense.


1. Core Premise

Large language models are clerical cognition engines.

They compress text, extract structure, reorganize information, and compare documents.

They do not originate truth, exercise judgment, or determine correctness.

They reduce labor. They do not replace thinking.


2. Historical Constraint

Before cheap computation, reading large volumes was expensive, cross-checking sources was slow, and synthesis required staff.

Institutions therefore held advantages: think tanks, policy offices, PR operations, lobbying groups, major media.

Their edge was processing scale. They could read everything. Individuals could not.

Trust in authority became economically rational. Informational asymmetry emerged.


3. Mechanism of Propaganda

Propaganda exploits verification cost. It rarely depends on direct falsehood.

Common techniques: selective evidence, omission, framing, rhetorical emphasis, excessive volume, buried caveats.

Goal: make checking more expensive than trusting.

If verification costs hours, most people do not verify. Conclusion is accepted by default.

Therefore, propaganda is a cost strategy.


4. Capability Introduced by Local LLMs

Locally operated models reduce analysis cost to near zero.

They automate summarization, claim extraction, assumption listing, inconsistency detection, document comparison, and bulk compression.

Tasks that required interns now require seconds.

Result: processing ceases to be scarce. Judgment becomes the only bottleneck.


5. Requirement: Local Ownership

Cloud systems are insufficient. They are centralized, permissioned, rate-limited, revocable, and surveilled. They preserve dependency.

Structural change requires local execution, offline capability, private ownership, and no approval required.

Capability must exist at the household level. Only then does it become infrastructure.

Infrastructure spreads horizontally. Each person installs independently. No coordination required.


6. Structural Effect

When a defensive capability becomes cheap, standardized, mass-produced, and individually owned, institutional monopolies erode.

This is an economic effect, not a political one.

Local LLMs commoditize analysis. They eliminate the institutional monopoly on document processing.

Result: private audit becomes affordable. Authority loses automatic deference.


7. Operating Doctrine

Treat the model as clerk, research assistant, extractor, and critic.

Do not treat it as oracle, authority, companion, or autonomous thinker.

The model handles labor. The operator handles judgment.


8. Standard Audit Procedure

Given any institutional or persuasive document:

First, summarize content. Second, extract explicit claims. Third, extract implicit assumptions. Fourth, list unsupported assertions. Fifth, identify internal contradictions. Sixth, compare claims to independent sources. Seventh, reconstruct argument structure.

Time cost: minutes. Objective: personal clarity. No publication required. No persuasion required.


9. Scope

This is not counter-propaganda. It is unilateral defense.

Goal is not to change others' beliefs. Goal is: do not be misled yourself.

One person. One machine. One document.

Universal adoption is unnecessary. Availability alone alters incentives.


WARNINGS AND LIMITATIONS

All tools introduce new failure modes. The following constraints are mandatory considerations.


10. Warning: Synthetic Volume

LLMs reduce both analysis cost and production cost.

Adversaries can generate fake articles, fake comments, fake reviews, fake reports, and synthetic consensus.

Volume is no longer evidence. Do not equate repetition with truth.

Problem shifts from analysis to source selection.


11. Warning: Adversarial Narratives

Documents will be optimized to appear internally consistent, well-cited, formally structured, and audit-proof.

Consistency does not imply truth. The model checks structure, not reality.

Do not confuse coherence with correctness.


12. Warning: Provenance Failure

Fraudulent source documents can be fabricated cheaply. Fake studies and reports are trivial to produce.

Before analyzing content, verify origin, authorship, authenticity, and chain of custody.

If provenance is unknown, analysis is meaningless. Authentication precedes reasoning.


13. Warning: Operator Error

The model does not think. Operator mistakes dominate outcomes.

Common failures: asking vague prompts, accepting summaries uncritically, selecting biased comparison sources, outsourcing judgment to the model.

An uncritical operator with an LLM is merely a faster fool. Discipline is required.


14. Warning: Training Data Contamination

Model priors reflect training data. If the corpus contains propaganda, marketing, or low-signal internet text, analysis quality degrades.

Prefer textbooks, manuals, structured data, primary sources, and institutional statistics.

Signal quality dominates volume. Curate inputs.


15. Warning: Behavioral Bypass

The most likely failure mode is non-use.

If the model is used primarily for chatting, entertainment, companionship, or content generation, audit mode never activates.

The tool becomes distraction, not defense. Maintain deliberate use.


STRATEGIC GUIDELINES


16. Recommended Practices

Run models locally. Maintain curated corpora. Develop standard audit prompts. Verify provenance before analysis. Treat outputs as drafts, not answers. Keep human judgment final.


17. Non-Goals

Do not attempt persuasion campaigns, automated truth detection, full delegation of reasoning, or reliance on a single model.

The system is assistive only.


CONCLUSION

Large language models do not primarily grant influence. They remove dependence.

They do not create truth. They make verification affordable.

When verification becomes cheap, informational asymmetry collapses, authority loses automatic trust, and propaganda loses leverage.

Primary use: quietly check the work of those who never expected you to afford the bill for doing so.

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