anchor.diff:      list_append(VMC, fe(addressee, uriref(aiu:self))),
       list_append(VMC, frame(fnQuestioning)),
       
  -    fnvm_exec (en, I, VMC)
  +    fnvm_exec (I, VMC)
   ------------------------------------------------------------
   
   TIP: Since in production the actual translation from natural language to the initial prolog
   terms is done by a seq2seq neural net it is important to give it a target sequence to work with.
   In Zamia-AI this is done by having a flat, uniform layer2 processing as shown above. 
  -If you have multiple ''nlp_gen''s that you want to produce the same frame structure it is useful
  -to put that code into an intermediate rule instead of copy-pasting it into each ''nlp_gen''.
  +If you have multiple 'nlp_gen's that you want to produce the same frame structure it is useful
  +to put that code into an intermediate rule instead of copy-pasting it into each 'nlp_gen'.
   However this would mean the seq2seq net would only see a single target predicate so there is little
   it can learn from that. 
  -To resolve this issue use the ''inline()'' predicate to have a macro-like expansion if the called
  +To resolve this issue use the 'inline()' predicate to have a macro-like expansion if the called
   predicate. For an example of how this is done, see below.
   
   [source,prolog]
  
positive.code:         list_append(VMC, fe(spkr, USER)),
          list_append(VMC, frame(fnQuestioning)),
          
          fnvm_exec (en, I, VMC).
  
      nlp_gen(en, 'What (were we talking|did we talk) about (again|)?', 
              inline (l2proc_whatWasOurTopic)).
  
negative.code:   assertz(ias(I, rframe, RFRAME))
  
  - invoking layer 4 for adding actions appropriate to the response frame(s):
    l4proc (I)
  
  NOTE: both uframe and rframe generation are typically ambigous, as are all NLP tasks. so score points
        should be added as appropriate during processing so the outer framework can act the/one of the best
  