Introducing Miniscript - Maximizing Meaning by Minifying Language

Miniscript minimizes natural language prompts via abbreviation, prioritization, compression. Crystallizing directives inside model attention bandwidth, the compact style unlocks creative possibility within tight token constraints.

Introducing Miniscript - Maximizing Meaning by Minifying Language

Miniscript is a minimized style of natural language for prompt engineering, packing more directives into fewer characters for concise yet powerful AI instructions. Mastering a few core tactics opens creative possibility within constraints.

Techniques and Examples

Various minification techniques empower users to crystalize commands inside model attention bandwidth:

Abstraction - Broad descriptions efficiently signal wider concepts that specifics overlook. Ex. Analyze politically rather than Analyze 2024 US Presidential race

Prioritization - Lead with most directive terms first. Ex. Recommend comedy movies has higher relevance signals upfront versus I need funny movie suggestions

Compression - Remove inessential words not lowering meaning. For example system requirements → sysreqs.

These may also include:

Shortening Words & Phrases
Trim terminology to its essence without losing meaning. For example use acq rather than acquisitions. Apply judiciously balancing brevity and clarity.

Prioritizing Key Concepts
Lead prompts with most critical terms for maximum impact, as models focus on start and end.

Ex. Clsfy imagennet panda frog → Clsfy imagennet two mammals

Employing Symbols (& Abbreviations)
Special characters like @ convey information densely. Domain abbreviations provide efficiency like NLP for natural language processing.

Explain NLPaa pipeline @high level → ❓ NLPaa ♾ lvl

Strategic Dropping of Words
Remove unnecessary words if underlying meaning isn't reduced. Assess each term's necessity.

Ex. Summarize ongoing key events → Summ ongoing key

Syntax Optimization

Structure expressions considerately, using ":" or ";" for compact clauses while formatting paragraphs for scannability.

Refer to [[Data]]; Outline [[Insights]]

The shortened phrasing of Miniscript allows segmenting critical instructions both early, exploiting primacy bias, and late to counter recency effects for enhanced prompting balance. The condensed language bridges tradeoffs between template structure and flexibility. And with characters at a premium within token limitations, compact clarity saves budget for content.

Blending the readability of natural language with terseness bordering on shorthand, Miniscript hits an optimization sweetspot making the most of each word. The minimalist expression compels models to squeeze understanding from sparse sentences laden with implications. For prompt engineers, this fluency magnifies influence over AI systems within tight constraints.

Pocket Techniques for Minifying Natural Language

When crafting Miniscript prompts, a variety of minification tricks exist to concisely convey meaning:

  Shortened forms like AI or FAQ efficiently communicate expanded phrases artificial intelligence or frequently asked questions.

Ex - NLP algo analysis → NLPaa

Word/Phrase Shortening Trim terminology to its essence. E.g environmental sustainability → enviro-sust

Ex - Summarize key sections → Sum key secs

  Special characters inline concentrate meaning. Ex heart ❤️ evokes more feel than the word heart.

Ex - Topic requires ♾️ nuance ➕ sensitivity

Optimized Syntax
  Use compact code-inspired expressions suited for technical contexts.

Ex - [[Initialize variables a,b. Compute a+b]]

Strategic Cuts
  Remove inessential words if meaning isn't diminished.

Ex - Provide insightful literary critique → Provide insight crit

  Omitting vowels shortens while retaining legibility.

Ex - Evaluate ongoing effectiveness → Vlw8 ngng fctvns

Compact Languages
  Leverage languages like Headlinese that are highly condensed.

Ex - Global warming driving climate crisis mitigation urgency

Miniscript craftily utilizes all syntactic resources available to compress prompts for optimizing character economy and instruction impact. The stripped down phrasing makes each portion earn its place. Through practice, compact communication with AI systems becomes second nature rather than a restriction. Achieving explanatory prowess and persuasive punch in minimalist prompts ultimately liberates creativity.

Example of MiniScript Combined with PseudoScript


def AnalyzeRespond(post, resps, aud, plat) -> (list, resp): # Extract & Analyze Info # 1.1 Extract content, author, metadata from post & resps. # 1.2 Analyze tone, topic, big idea. # 1.3 Analyze aud demographics, interests, preferences. # 1.4 Identify trends, plat algorithms. # Emotional & Visual Appeal # 2.1 Craft response to evoke emotions, use storytelling. # 2.2 Include visuals, clean format. # 2.3 Personalize, share relatable experiences. # Generate & Refine Content # 3.1 Generate questions & response from analysis. # 3.2 Refine for clarity, relevance, humor, wit, creativity. # 3.3 Fact-check, cite sources. # 3.4 Incorporate feedback, adjust approach. # Optimize for Shareability & Inclusion # 4.1 Use trending keywords, hashtags, plat features. # 4.2 Encourage engagement, call-to-action. # 4.3 Be mindful of diversity, avoid offensive content. # 4.4 Consider timing for visibility, shareability. # Return Questions & Response # 5.1 Return finalized, formatted questions & response.


def AnlzRspnd(pst, rsps, aud, plt) -> (list, rsp): # Ext & Anlz Info # 1.1 Ext cntnt, authr, mtdt frm pst & rsps. # 1.2 Anlz tne, tpc, bg id. # 1.3 Anlz aud dmgrphcs, intrsts, prfrncs. # 1.4 Idntfy trnds, plt algrthms. # Emo & Vis Apl # 2.1 Crft rsp to evk emtns, strytllng. # 2.2 Incl vsuls, cln fmt. # 2.3 Prsnlz, shr rltbl exp. # Gnrt & Rfn Cntnt # 3.1 Gnrt qs & rsp frm anls. # 3.2 Rfn fr clrt, rlvnc, hmr, wt, crtvty. # 3.3 Fct-chk, ct srcs. # 3.4 Incrpr fbk, adjst apprch. # Optmz fr Shrblty & Inc # 4.1 Us trndng kywrds, hshg, plt ftrs. # 4.2 Encg engmt, cta. # 4.3 Cnsdr dvrsty, avd offns cntnt. # 4.4 Cnsdr tmng fr vsblty, shrblty. # Rtn Qs & Rsp # 5.1 Rtn fnlz, fmt qstns & rsp.

Benefits of Miniscript Compression

Miniscript provides ways to concisely convey complex instructions by minifying natural language down to compact syntax. Consider the code snippet above outlining an article generation workflow. Miniscript allows packing extensive details in limited space through:

  Careful word choice and symbolic expressions communicate exact intents compressed for accuracy. For example, plt algorithms targets recommendation systems precisely.

  More directives fit per character, increasing information density. Complete analysis, writing and optimization steps covered in one condensed flow.

  Numbered hierarchy creates visual structure from bird's eye view despite brevity up close, enabling quick scanning.

Adaptability Mix abbreviated words, acronyms, symbols and code alike for flexible reduction while retaining understandability. Ex: rspns = responses

Process Mapping
  Each line encapsulates an entire process segment that can link to module code, blueprinting workflows.

Capacity Planning Token limits visible upfront, allowing planning longer instructions through tighter sentences rather than omission regrets mid-prompt.

By packing meaning into minimized syntax, Miniscript prompts make the most of each character, conveying structured thinking in constrained spaces. The compression centers attention while empowering ambition - opening creative possibilities within limitations. In an AI world where message precision and code transparency matter more than ever, Miniscript distills communication to its essence.

Miniscript and RAG - Compressing Complexity

As recursive prompt expansion techniques like RAG (Retrieve and Apply Generation) scale information packed into instructions, balancing clarity with conciseness grows challenging. Lengthy appended context windows risk diluting core directives in a flood of text. This is where Miniscript’s compact syntax shines - preserving prompt precision and potency even as supplementary content accumulates.

Miniscript provides a structured blueprint compressing multi-step goals that RAG enhances with relevant contextual details. The minimized language framework sets clear intentions upfront that persist as supplemental facts get retrieved and woven in behind the scenes. Rather than meandering verbosity, continuities of purpose resound clearly.

For example, a Miniscript process:

# Anlyz oprx costs, proj rev # Recm optimize op budget

Gets expanded by RAG into:

# Anlyz oprx costs, proj rev Over the last 3 quarters, operating expenses have increased 12% while revenue declined 5% due to supply chain bottlenecks and inflationary pressures:[Context data retrieved]  # Recm optimize op budget

The compressed initial instructions endure, ensuring the model's task awareness persists as the knowledge artifact grows lengthy.

Blending RAG’s augmentation capabilities with Miniscript’s densely packed directives thereby unlocks AI potential otherwise hampered by diffused attention. Each approach complements the other - enabling both rich context and concise control right-sized for language models to absorb and act upon strategically.

Trying Miniscript
Start by minifying a lengthy prompt of yours. Observe maximal meaning retained despite fewer words. Build skill compacting ideas over time.

The purpose of Miniscript isn't syntactic tricks but strategically engineering efficient communication. Compression centers focus for AI systems, boosting relevance - a vital skill as models grow more capable. Practiced promptly, minimizing language unlocks otherwise unrealizable creativity.

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